Fusion

arXiv:2607.15072v1 Announce Type: new Abstract: Total-f five-dimensional gyrokinetic simulations are essential for self-consistent studies of multi-scale, multiphysics transport in the edge region of diverted tokamak plasmas. However, conventional initialization with a local Maxwellian distribution often generates large-amplitude transients, particularly geodesic acoustic modes (GAMs). These transients are especially severe in the plasma edge because of steep profile gradients, strong radial electric fields, and high safety factors, and they increase the computational time required to reach a saturated turbulent state. To address this problem, we present a new initialization scheme for the total-f XGC code that uses a relaxed particle distribution obtained from a computationally inexpensive axisymmetric simulation. Before the distribution is transferred to the full turbulence simulation, phase-space smoothing is applied to reduce particle noise while preserving its neoclassical

arXiv:2607.14722v1 Announce Type: new Abstract: We present magnetic configurations in the Helically Symmetric eXperiment (HSX) and Wendelstein 7-X (W7-X), in which the edge magnetic structure is dominated by island chains which are spatially larger than the previously reported configurations. These ``giant" island chains (with rotational transform $\iota=4/3$ or $4/4$ for HSX and $\iota=5/6$, $5/5$ or $5/4$ for W7-X) are obtained by reducing the coil current in main coil 6 for HSX and non-planar coil 5 for W7-X (i.e. the coil nearest the up-down symmetric cross-section $\phi=36^\circ$ for W7-X and $\phi=45^\circ$ for HSX); this appears a sufficient (but not necessary) condition for giant islands. The giant islands create relatively straight X-point legs which transport plasma to the plasma-facing components (PFCs). In the most extreme cases, the island O-points leave the domain of the field line map and the divertor legs of the remaining ``unpaired" X-points do not close around the

arXiv:2607.14496v1 Announce Type: new Abstract: We present VEQ-MF, a fast spectral parameter-scan framework for two-dimensional axisymmetric multi-fluid equilibria with prescribed species-dependent toroidal rotation. The solver couples generalized Boltzmann density responses, quasineutral electrostatic polarization, and a generalized Grad--Shafranov equation, extending reduced-parameter Grad-Shafranov and VEQ formulations to multi-species rotating equilibria. Rotating $p\text{-}^{11}\text{B}$ spherical-tokamak configurations are used as a demanding test case. Independent scans of the proton and boron rotation frequencies are performed in EHL-2 and EHL-3B geometries. The computed fields are then post-processed to obtain fusion power from a drift-Maxwellian reaction-rate coefficient and bremsstrahlung power from an analytical radiation model. Three in-range EHL-3B finite-difference benchmarks give global stored-energy, bremsstrahlung-power, and fusion-power differences of

arXiv:2607.14146v1 Announce Type: new Abstract: Tokamak--stellarator hybrids seek to combine tokamak-like compactness and confinement with stellarator-like externally generated rotational transform and steady-state operation. In this work, we build on the recent tokamak--stellarator hybrid study using planar dipole-field coils (PDCs) [Yu et al., arXiv:2605.03599], in which the fixed-position, programmable coils on an axisymmetric winding surface generate flexible three-dimensional shaping fields. Using single-stage free-boundary optimization of coil currents and plasma-equilibrium parameters, we construct vacuum and finite-$\beta$ configurations. The vacuum cases show controllable external transform and magnetic well. The finite-$\beta$ cases accommodate various density, temperature, and pressure profiles, producing quasi-axisymmetric (QA) equilibria with self-consistent bootstrap current, favorable Mercier stability, and reduced demand for external current drive. Re-optimization

arXiv:2607.14908v1 Announce Type: new Abstract: Deploying Video Diffusion Models (VDMs) on edge devices is appealing for localized and privacy-preserving generation, but their iterative Transformer-based denoising remains too slow for practical local inference. Cross-Timestep Caching (CTC) has emerged as a promising direction for reducing redundant computation, reusing activations across adjacent denoising steps rather than modifying model weights, while largely preserving generation fidelity. However, on memory-constrained edge GPUs, CTC requires a massive cache footprint that quickly exceeds on-device VRAM and forces the cache into host memory. More fundamentally, cache operators remain tightly interleaved and chain-dependent with native compute operators, so naive near-memory offloading still incurs repeated PCIe exchanges for residual and fusion computations, turning cache reuse into a communication- and serialization-bound execution flow. We therefore propose CODA, an

University of Arizona researchers have demonstrated a promising new application for graphene nanoribbons, a nanoscale semiconductor material with the potential to withstand extreme environments. The team's findings could help clear a key hurdle to bringing fusion energy to the electric grid.

arXiv:2607.13390v1 Announce Type: new Abstract: I-mode is a promising confinement regime for future fusion reactors because it combines enhanced energy confinement with L-mode-like particle transport and naturally ELM-free operation. Previous EAST I-mode studies were performed exclusively under lithium-conditioned wall conditions. Here we report the first systematic experimental investigation of I-mode under boronized wall conditions on EAST and compare it with an existing lithium-conditioned I-mode database at the same toroidal field, $B_t = 2.47$\,T. The boronized-wall dataset exhibits a substantially broader accessible density range, with the Greenwald fraction extending from $f_{\mathrm{GW}} = 0.26 - 0.77$ , compared with $f_{\mathrm{GW}} = 0.35 - 0.54$ under lithiation. A higher normalized $\mathrm{D}_\alpha$ emission suggests that enhanced edge recycling may contribute to this density extension. A striking increase in favorable-configuration I-mode is also observed: $51\%$

arXiv:2607.12905v1 Announce Type: new Abstract: The transport of runaway electrons (RE) in ergodic magnetic geometries is an area of active study. Computing the transport from the direct simulation of particle trajectories is computationally expensive. Instead, diffusion models, such as the one by Rechester and Rosenbluth, are often employed to incorporate transport effects into reduced simulations. However, the comparison of diffusion-based to direct simulations reveals that the transport is typically not purely diffusive. In this paper, we introduce a simple transport model, based on chaos theory, which goes beyond the Rechester-Rosenbluth approximation. Besides chaotic diffusion, our model takes into account the effect of so-called sticky regions, a trapping layer around magnetic islands, where particle escape slows down to a power-law decay rather than an exponential decay. We demonstrate the applicability of the model both in the Ullmann-Caldas map with parameters corresponding

arXiv:2607.12512v1 Announce Type: new Abstract: Fast predictive modelling of radio-frequency heating and current drive is important for integrated tokamak scenario design, yet kinetic calculations of helicon-wave absorption remain too computationally expensive for large-scale parameter scans. We present a reduced model for helicon-wave heating and current drive that retains the dominant parallel electron Landau-damping channel. The wave response is evaluated on the cold-plasma dispersion root, and a single-Landau-pole correction is introduced to obtain compact expressions for the local damping rate and current-drive efficiency. The model is benchmarked against the Chiu-Chan heating model using approximately 1.6 million samples covering representative conditions of EAST, HL-3, DIII-D and KSTAR. The reduction error is found to be governed primarily by the electron Landau parameter and electron beta. Within an identified sub-lower-hybrid-frequency validity window, results from different

arXiv:2607.12272v1 Announce Type: new Abstract: When the guiding-center description fails and the full gyromotion must be resolved for energetic particles in complex configurations like stellarators, charged-particle integrators must be formulated directly in the curvilinear flux coordinates. The Boris algorithm, which adopts a staggered scheme in Cartesian coordinates, is phase-space-volume-preserving and second-order accurate; but a direct port to flux coordinates degrades the position update to first order, because the evolving basis vectors of the curvilinear frame make the starting-point metric deviate from the ideal midpoint metric. We construct a collocated, midpoint-predicted Boris algorithm in flux coordinates, restoring second-order accuracy at the cost of one additional field evaluation per step. In reactor-scale stellarator magnetic fields, the scheme recovers second-order convergence in every coordinate component, retains near-machine-precision energy conservation and a

arXiv:2607.11915v1 Announce Type: new Abstract: Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data. In practice, fusion diagnostics fail regularly: acquisition systems start late, individual sensors die, and signal dropouts cluster precisely when a plasma disruption is approaching. We present the first systematic robustness benchmark for plasma diagnostic ML using the TokaMark dataset of 11,573 MAST shots, evaluating XGBoost, LSTM, Transformer, and the TokaMark CNN baseline across six physically-grounded failure scenarios and three imputation strategies. We introduce the Robustness Score (RS) for standardized cross-architecture comparison. Our central finding is that disruption-proximate sensor failure (corruption injected in the final window timesteps) collapses sequence model performance (LSTM +212% NRMSE) while a statistical feature model remains comparatively stable (XGBoost +37%). Forward-fill imputation eliminates

arXiv:2607.11915v1 Announce Type: cross Abstract: Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data. In practice, fusion diagnostics fail regularly: acquisition systems start late, individual sensors die, and signal dropouts cluster precisely when a plasma disruption is approaching. We present the first systematic robustness benchmark for plasma diagnostic ML using the TokaMark dataset of 11,573 MAST shots, evaluating XGBoost, LSTM, Transformer, and the TokaMark CNN baseline across six physically-grounded failure scenarios and three imputation strategies. We introduce the Robustness Score (RS) for standardized cross-architecture comparison. Our central finding is that disruption-proximate sensor failure (corruption injected in the final window timesteps) collapses sequence model performance (LSTM +212% NRMSE) while a statistical feature model remains comparatively stable (XGBoost +37%). Forward-fill imputation

arXiv:2607.11789v1 Announce Type: new Abstract: Local flux-tube gyrokinetic simulations of ion-scale turbulence in tokamak plasmas at finite plasma beta are conducted to investigate the generation of zonal flows via turbulent stresses. A parameter scan in the safety factor $q$ and electron beta $\beta_e$ reveals a transition from low- to high-transport states when $\beta_{\mathrm{eff}} \equiv q^2\beta_e$ exceeds a certain critical value $C_{\mathrm{nl}}$. While the linear stability limits for kinetic and ideal ballooning modes also scale as $\beta_e \propto 1/q^2$, they lie above the observed transition, indicating that the effect is not due to linear instabilities but to nonlinear dynamics. At low $\beta_{\mathrm{eff}}$, Reynolds stress dominates and drives zonal flows. At higher values, Maxwell stress becomes comparable, suppressing zonal-flow formation and leading to divergent transport. This nonlinear-transition boundary is determined for both the Cyclone Base Case and a spherical

arXiv:2607.11784v1 Announce Type: new Abstract: Equilibrium sheared $\vec{E}\times \vec{B}$ flow, a standard cure for plasma turbulence, can backfire. In gyrokinetic simulations of a newly identified regime, imposed shear comparable to the intrinsic zonal shear destroys the self-generated zonal flows regulating the turbulence: transport rises sharply before stronger shear quenches it. A reduced fluid model traces this to the incompatibility of imposed and zonal shear layers. Simulations of spherical tokamak discharges place the inferred rotation shear at, or just below, the threshold of the sharp transport increase, implying that the toroidal rotation may be limited mainly by the heat, not momentum, injection.

arXiv:2607.11208v1 Announce Type: new Abstract: The VeloAlpha System Code (VSC) is a computational framework for zero-dimensional fusion power-balance studies across five magnetic-confinement configurations: tokamaks, magnetic mirrors, field-reversed configurations (FRCs), dipoles, and stellarators. A common power-balance formulation connects fusion production, charged-particle deposition, radiation, transport loss, external heating, and fusion gain, while each configuration retains its own geometry, profile weights, confinement model, and operating constraints. The same solver interface supports both single-point calculations and two-dimensional plasma operating contour (POPCON) scans, producing fusion and heating powers, gain, radiation and transport losses, geometry quantities, and configuration-specific validity indicators. VSC therefore makes it possible to study how assumptions about density, temperature, magnetic field, confinement, and geometry shape the accessible operating

arXiv:2607.11045v1 Announce Type: new Abstract: Hot electrons generated in laser-produced plasmas are a central focus in inertial confinement fusion, laboratory astrophysics, and high-energy-density physics. These electrons originate from instabilities in nonlinear laser-plasma interactions, which are critically modulated by laser bandwidth. Here, we experimentally demonstrate enhanced generation of hot electrons by utilizing instantaneous low-coherence lasers with two bandwidths (0.2% and 0.6%) at intensities of 2-8x10^{14} W/cm^2 and energies up to 620 J. A significant enhancement of hot electron temperature and hard X-ray yield is observed with the broadband lasers compared to a conventional narrowband laser. The results show that the hot electron energy conversion efficiency of the 0.6% broadband laser is approximately 4 times higher than that of the narrowband laser, reaching a maximum value of 2.8%. These findings validate a moderate-bandwidth laser as an efficient hot electron

arXiv:2607.10807v1 Announce Type: new Abstract: Luminescence thermometry has evolved through decades of research focused on optimising materials and on extracting temperature information from isolated spectral features such as luminescence intensity ratios, bandwidth, line shift and excited-state lifetime. Despite extensive material development, these conventional methods remain fundamentally limited by construction: only a small subset of pre-selected spectral features is exploited, while the bulk of the temperature-relevant information encoded in the full spectrum is systematically discarded. A paradigm shift is presented here, Luminescence Thermometry 2.0 (LT 2.0), implemented through the newly developed SpectraSensML platform, in which machine learning regression operates on the entire emission spectrum to deliver temperature readout. The approach is demonstrated on a Yb3+-doped phosphor emitting in the near-infrared biological transparency window across 125 to 700 K. Nineteen

arXiv:2607.11279v1 Announce Type: new Abstract: The widespread adoption of social media platforms has transformed online communication by enabling users to exchange information and opinions instantly. However, these platforms have also facilitated the dissemination of abusive and hateful content, posing major social, psychological, and ethical challenges. Hate speech can incite discrimination, harassment, and violence against individuals or communities based on attributes such as ethnicity, religion, gender, nationality, or political affiliation. Consequently, automatic hate speech detection has become a major research topic in natural language processing (NLP) and an essential component of content moderation systems. This paper investigates automatic hate speech detection in the Algerian Arabic dialect (Darija) on social media. This task remains challenging because of the dialect's linguistic diversity, characterized by the coexistence of Arabic, French, and Arabizi (Arabic written

arXiv:2607.10866v1 Announce Type: new Abstract: This work is concerned with the development of control strategies for the Direct Internal Recycling Loop (DIRL) system, which is an essential part of the Tritium Fuel Cycle (TFC) for the fueling of fusion reactors. As a first step, a control-oriented model is developed that describes dynamic behavior of DIRL with interactions between the torus reactor, buffer and fuel units, and recirculation streams. This model is used to evaluate the controllability, stability of the DIRL, and interactions between input and output variables. Moreover, the direct recycling of isotopes from the exhaust gases is discussed from a control perspective. It is observed that Gas Distribution and Storage (GDS) within the DIRL is associated with significant control challenges due to input-output interactions and competing process objectives. Three control strategies are developed and evaluated for the GDS of the European Demonstration fusion power plant

arXiv:2607.10740v1 Announce Type: new Abstract: The analysis of Multivariate Time Series (MTS) plays an important role in a lot of real-world practical applications, but it still remains some challenging problem about capturing multi-granularity structural patterns and suppressing noise appropriately. Multi-Scale Convolution with Optimal Transport Attention (MSC-OT) is proposed in this paper. MSC-OT is a useful architecture to optimize the attention mechanism. It combines multi-scale convolution with Sinkhorn optimal transport method based on inverted embedding. The inverted embedding approach embeds each variable as a token and allows the model to capture cross-variate relationships better. MSC-OT consists of two part: (1) Multi-Scale Convolution Enhancement, that applies multi-scale convolutions to attention score matrices based on inverted embedding, capturing local structural patterns in the variate-interaction space induced by compressed temporal representations; (2) Sinkhorn

arXiv:2607.10544v1 Announce Type: new Abstract: 3D point cloud anomaly detection plays a vital role in industrial manufacturing, yet it faces significant challenges due to the scarcity and high acquisition cost of real anomalous samples. The inherently anomaly-free training data further hinders detection methods from effectively learning discriminative features between normal and abnormal instances. To address these issues, we propose PA3AD, a novel framework that introduces a physics-inspired pseudo-anomaly generation strategy to create physically plausible anomalous samples from normal data. Additionally, we incorporate prototype features via a weight-sharing mechanism to guide the model in capturing the distribution shifts between normal and anomalous samples. Specifically, PA3AD introduces two key innovations to tackle the scarcity of real anomalies. First, a physics-inspired module generates diverse pseudo-anomalous point clouds from normal data via multi-physics modeling.

arXiv:2607.09088v1 Announce Type: new Abstract: As fusion energy moves from theoretical feasibility toward commercialization, design of new reactor concepts, autonomous tokamak control, and high-performance scenario optimization are becoming increasingly important. Traditionally, such optimization tasks have relied on costly trial-and-error or brute-force parameter searches, based on black-box experiments or simulations. Recently, advances in differentiable programming are changing the paradigm of numerical simulation. Unlike conventional simulations, which are typically executed as locally connected step-by-step procedures, differentiable simulation represents the entire simulation pipeline as a connected computational graph. In such a framework, machine parameters, actuator waveforms, and plasma responses are linked through differentiable operations, allowing Jacobians to propagate across the full simulation. This enables direct gradient-based control and optimization using the

arXiv:2607.09088v1 Announce Type: cross Abstract: As fusion energy moves from theoretical feasibility toward commercialization, design of new reactor concepts, autonomous tokamak control, and high-performance scenario optimization are becoming increasingly important. Traditionally, such optimization tasks have relied on costly trial-and-error or brute-force parameter searches, based on black-box experiments or simulations. Recently, advances in differentiable programming are changing the paradigm of numerical simulation. Unlike conventional simulations, which are typically executed as locally connected step-by-step procedures, differentiable simulation represents the entire simulation pipeline as a connected computational graph. In such a framework, machine parameters, actuator waveforms, and plasma responses are linked through differentiable operations, allowing Jacobians to propagate across the full simulation. This enables direct gradient-based control and optimization using the

arXiv:2607.09346v1 Announce Type: new Abstract: A fixed boundary stellarator equilibrium capable of producing 3 GW of fusion power (1 GW-electric) is presented as the design basis for the GIGA fusion power plant being developed by Gauss Fusion GmbH. The stellarator concept provides a steady-state, transient free, low recirculating power approach to a fusion power plant, which builds on 50 years of progress in plasma physics. A set of requirements for a fixed boundary equilibrium were determined through application of 0.5 D modeling. Optimization of a modified Wendelstein 7-X (W7-X) equilibrium was performed to achieve these requirements including alpha power confinement greater than $85\%$, neoclassical effective ripple below 0.01, bootstrap current below 50 kA, and reduced turbulent heat fluxes. In order to fix the plasma volume of $1500~m^3$ during optimization, the VMEC code was modified to renormalize the boundary coefficient to the desired plasma volume. The STELLOPT stellarator

arXiv:2607.08036v1 Announce Type: cross Abstract: Magnetic geometry shapes how turbulence transports and dissipates energy in strongly magnetized plasmas. The solar corona, a maze of open and closed flux tubes with sharp transverse gradients, is a prominent example, yet most wave-turbulence models of coronal heating assume symmetric flux tubes or add geometric effects in ad hoc ways. Here we develop a geometry-complete multiscale transport theory for reduced-magnetohydrodynamic turbulence in an arbitrary background field, retaining squashing (magnetic shear), transverse gradients, curvature, and gravity at the same order as standard expansion-driven reflection, and coupling fast, anisotropic fluctuations to slow background evolution through conservation laws. Applied to the corona, it recovers the standard reflection-driven turbulent cascade in smooth regions such as coronal-hole interiors, but predicts that in structured regions geometry-driven channels can dominate: squashing drives

arXiv:2607.08501v1 Announce Type: new Abstract: A critical requirement for MITICA -- a full-scale prototype of the heating Neutral Beam Injectors hosted at the Consorzio RFX Neutral Beam Test Facility for the ITER experiment -- is the capability to withstand a continuous voltage of 1MV across the vacuum gaps insulating the beam source from the grounded vessel. To validate such feature, a dedicated voltage-holding test campaign was conducted throughout 2024 and 2025 using a full-scale mock-up of the beam source. The tests also involved an accurate characterization of the associated breakdown events: vacuum dielectric failures which result in rapid potential drops and generate strong current discharges. This contribution will present a relative time reconstruction architecture based on cost-effective, embedded RedPitaya (Zynq-7000 FPGA) devices repurposed as timing hubs. These nodes function as configurable trigger multiplexers while simultaneously recording trigger signals as

arXiv:2607.08290v1 Announce Type: new Abstract: The influence of ferritic steel on low-shear stellarator/heliotron magnetic configurations is investigated for the Heliotron J device using a point dipole magnetisation model. By numerically evaluating ferritic steel plates assumed at several locations inside the Heliotron J vacuum vessel, the changes in the rotational transform and magnetic island width are shown to be sensitive to the installation location. This sensitivity arises from the coupling between the background nonaxisymmetric field and ferrite perturbation, rather than being determined solely by the perturbation amplitude. Ferritic steel plates placed on the outer side of a straight section produce the most significant changes in the magnetic topology and exhibit the highest sensitivity to violations of the $M = 4$ toroidal periodicity. Additionally, we show that appropriate arrangements of passive magnetic dipoles can reduce the effective helical ripple while preserving the

arXiv:2607.08234v1 Announce Type: new Abstract: Real-world time series exhibit complex dynamics characterized by multiple simultaneous temporal patterns: short-term fluctuations, periodic seasonal cycles, long-term trends, and irregular abrupt changes. However, many existing forecasting architectures rely on single-path temporal modeling--transformers capture long-range dependencies but smooth local variations, convolutions capture local patterns but have limited receptive fields, and linear models are efficient but cannot capture nonlinear dynamics. To address this, we introduce RhyMix (RHYthm MIXture), a hybrid neural architecture designed around a parallel dual-path modeling paradigm with adaptive gating mechanisms. RhyMix integrates two complementary encoding branches: (i) a Cyclic Path that incorporates explicit seasonal inductive bias through learnable cyclic embeddings, capturing predictable rhythmic patterns; and (ii) a lightweight Multi-Scale Temporal Convolutional Network

arXiv:2607.08076v1 Announce Type: new Abstract: The complementary information between RGB and IR images can significantly enhance object detection performance under extreme conditions. Existing methods prefer dual-stream CNN backbones built upon YOLO for feature extraction and focus on the design of feature fusion. In this paper, we introduce the Laplacian Decoupled Feature Enhancement block (LDFE) to fuse features from different stages of the dual-stream CNN backbone. By design, LDFE simultaneously considers the characteristics of modalities and structures for feature fusion by employing global-local decomposition, denoising, fusion, and reconstruction, sequentially. The LDFE first separates features into global and local components based on Laplacian Pyramid, and then performs denoising and fusion based on Global State Space Enhancement module (GS2E) and Local Convolutional Correlation Enhancement module (LC2E) separately. Specifically, the GS2E conducts a two-branch architecture

Nuclear fusion inches closer after scientists combine supercomputing, AI and quantum computing to blueprint a way to create more tritium.

arXiv:2607.07161v1 Announce Type: new Abstract: The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy. To address this, we propose a novel, end-to-end adversarial spatio-frequency refinement network (ASFR-Net). Initially, a modality-invariant representation learner (MIR-Learner) guides the backbone to extract modality-invariant features, effectively bridging the primary domain gap. Subsequently, to address persistent residual modal differences, we design an innovative spatio-frequency synergistic enhancement module (SFEM), which identifies and suppresses sensor-specific noise and artifacts that are difficult to discern in the spatial domain by leveraging frequency-domain processing. Multi-level difference features are

Plasma pinches: From pursuits of nuclear fusion to an attractive point source of accelerated protons for proton radiography.

arXiv:2607.05749v1 Announce Type: new Abstract: The support structure is an integral part of the design of nuclear fusion reactors, especially 3D stellarator devices. A practical reactor's coils and support structures must have three competing qualities: an accurate magnetic field for good confinement, sufficient rigidity to protect the brittle high-temperature superconductor (HTS) from damage, and a simple geometry for low-cost construction. In existing devices, the coil geometry is often optimized without knowledge of the support structures' design and the coils' true stress and deformation. The support structures are then placed by hand through repeated finite element analyses (FEA) until engineering requirements are met. This makes the structural design of stellarator coil systems lengthy and labor-intensive. Using new developments in differentiable structural mechanics, we present coil-fem, an open-source software tool that integrates support differentiable FEA into the

arXiv:2607.05989v1 Announce Type: new Abstract: The convergence of Information Technology and Operational Technology has exposed Industrial Control Systems (ICS) to multi-stage cyberattacks that traverse software, network, and physical process layers simultaneously. Although Provenance-based Intrusion Detection Systems (PIDS) are effective in Information Technology (IT) environments, their applicability to Industrial Cyber-Physical Systems (CPS) remains largely unexplored because of the absence of datasets that jointly capture host-level causal behavior, industrial network semantics, and physical process state. To address this gap, we design an open-source, Hardware-in-the-Loop (HIL) CPS testbed that replicates an industrial chemical reactor control architecture across the Purdue model layers. Using this testbed, we propose ProvICS, a multimodal provenance dataset purpose-built for CPS intrusion detection, which synchronously captures four streams: whole-system provenance graphs from

arXiv:2607.05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics. In this work, we introduce a parameter-efficient Tri-Branch Modular Fusion Neural Network that synthesizes three orthogonal modalities: 3D spatial geometry (SchNet), discrete topological grammar (SMILES via ChemBERTa), and explicit macroscopic physicochemical descriptors (Deep & Cross Network). By bypassing standard scalar readouts and employing a shared late-fusion architecture, the framework establishes a mathematically rigorous multimodal latent space that effectively resolves the arithmetic and oversmoothing limitations of local message passing. We evaluate the proposed architecture on the QM9 benchmark, targeting the extensive thermodynamic property of atomization energy at 0 K ($U_0^{\mathrm{atom}}$). Through

A team of scientists from Oak Ridge National Laboratory, Cleveland Clinic and IBM has calculated nine molecular configurations of a promising material to produce fuel for fusion energy—the first known instance of such computations on quantum computers.

arXiv:2607.04865v1 Announce Type: new Abstract: This work presents deep learning models to predict magnetohydrodynamic equilibrium parameters and profiles for the ADITYA-U tokamak. A synthetic free-boundary equilibrium dataset consisting of 100,760 cases was generated using the pyIPREQ Grad-Shafranov solver, with inputs derived from 766 ADITYA-U plasma discharges and constrained to experimentally relevant circular limiter plasmas near the flat-top phase. Several deep learning approaches were investigated for predicting scalar equilibrium quantities, one-dimensional safety factor profiles and two-dimensional poloidal flux profiles. These approaches included Dense neural networks, principal component analysis based reduced-order models, one-dimensional and two-dimensional convolutional neural networks, and physics-informed neural networks incorporating Grad-Shafranov residual constraints. In addition, an inverse model was developed to estimate poloidal field coil currents from desired

arXiv:2607.04390v1 Announce Type: new Abstract: Plasma in nuclear fusion reactors is only partially observable: diagnostics are constrained by limited access, cost, and the harsh plasma environment, while high-fidelity simulations remain prohibitively costly at reactor-relevant scales to address the observability gap. This paper presents an ML model for reconstructing full-domain plasma states from a small number of accessible measurements. The model combines temporal encoding of sparse sensor histories with spatial decoding into complete plasma-field maps. Demonstrations using high-fidelity kinetic simulation data show that multiple coupled plasma quantities can be reconstructed from only a few density sensors, with robustness to sparse and randomly located probes. The approach provides a route toward diagnostic augmentation, real-time state estimation, and data-driven digital twins for fusion-relevant plasmas.

arXiv:2607.04026v1 Announce Type: new Abstract: Robust control of plasma current ramp-up is an absolute necessity, as an efficient and uncontaminated plasma current ramp-up is essential for achieving prolonged, high-pressure tokamak plasma discharges. In conventional tokamaks with Ohmic breakdown, the plasma current ramp-up is achieved primarily with pre-fixed temporal profiles of the applied toroidal electric field and the equilibrium magnetic field (Bv). The pre-fixed temporal profiles of these fields are often insufficient to maintain a successful plasma current ramp-up, as several unquantified dynamical variables, such as the condition of the vessel wall and plasma-facing components, influence the plasma current rise. Fuel gas injection in an appropriate quantity at a suitable time during the current ramp-up is therefore used to control the plasma current rise rate, ensuring successful plasma current start-up in Aditya-U. The gas injection time and gas quantity are controlled

Proxima Fusion has raised $468 million as it looks to move towards commercializing the promising but infamously difficult technical challenge of nuclear fusion.

Proxima Fusion has raised $468 million as it looks to move towards commercializing the promising but infamously difficult technical challenge of nuclear fusion.

arXiv:2607.04541v1 Announce Type: new Abstract: Camera-radar (CR) fusion is a practical sensing configuration for autonomous driving, but existing models are typically trained with task-specific supervision, limiting reusable representation learning. We present CRISP, a spatiotemporal CR backbone pretrained through forecasting-based representation learning. Given historical multi-view images and radar sweeps, CRISP learns a unified bird's-eye-view (BEV) representation by predicting future LiDAR point clouds. LiDAR is used only as privileged supervision during pretraining; the deployed model requires only camera and radar. To make forecasting-based pretraining effective for CR fusion, CRISP introduces an enhanced radar encoder, radar-enhanced temporal self-attention, and multimodal feature rendering with modality innovation gating. These components inject radar range and Doppler cues into BEV temporal propagation and allow BEV tokens to selectively incorporate camera and radar

arXiv:2607.04241v1 Announce Type: new Abstract: Plasma disruption is a critical threat to tokamak safety. Existing data-driven predictors mainly rely on time-series diagnostic signals, while visible images provide complementary spatial cues including plasma deformation, local brightening, and radiation-structure evolution. Although the image modality improves the model's discriminative capability, it also substantially increases the computational cost during inference. To address this issue, we propose a hierarchical multi-to-single-modal knowledge distillation framework for disruption prediction on a synchronized EAST multimodal dataset. During training, visible images and time-series signals are used to train a multimodal teacher, which learns disruption precursor representations through Transformer-based encoders and a prototype-guided spatiotemporal hypergraph module. During inference, only the time-series student is retained, with multimodal knowledge transferred through

arXiv:2607.03760v1 Announce Type: new Abstract: The deployment of large-scale foundation models like Segment Anything Model (SAM) on resource-constrained Earth observation platforms is hindered by prohibitive computational costs and the domain shift between natural and remote sensing imagery. To address these challenges, we propose \textit{Geo}spatial \textit{S}egment \textit{A}nything \textit{M}odel-Lite (GeoSAM-Lite), a lightweight, prompt-free segmentation framework designed for efficient onboard remote sensing segmentation. GeoSAM-Lite incorporates two core innovations: (1) Geospatial-Domain Initialization (Geo-Init), a domain-aware pre-training strategy that distills geospatial priors from a specialized teacher to bridge the domain gap; and (2) Feature Fusion Layers (FFL), which recalibrate spatial features and restore high-frequency boundary cues to overcome the capacity bottlenecks of lightweight backbones. Experiments across representative datasets, with a primary focus on

As we scour and scorch the Earth for deeper wells of energy, investors and government agencies are pouring

arXiv:2607.02394v1 Announce Type: new Abstract: Contrast-enhanced spectral mammography (CESM) acquires two images of each breast, a low-energy image and a recombined contrast image, but two questions central to building a classifier on them remain unsettled: whether the two image types carry comparable malignancy signal, and how a patient's several images should be combined into a single decision. Both are hard to answer reliably, because most published CESM classifiers split cross-validation folds at the image level, letting images of the same patient fall in both training and test sets and inflating reported performance. We pair a masked complex non-decimated wavelet feature bank with an elastic-net logistic classifier, evaluated under repeated patient-grouped nested cross-validation with patient-cluster bootstrap inference on the CDD-CESM dataset (1,880 images, 308 patients); under this leakage-free evaluation the inflation from testing on previously seen patients is negligible. On

arXiv:2607.01488v1 Announce Type: new Abstract: A specially shielded CdTe detector based hard X-ray (HXR) monitoring system equipped with a lead collimator has been developed and installed on the Aditya-U tokamak to investigate the dynamics of fast electrons (~20-200 keV) generated during sawtooth activity. The pre-existing HXR monitor in Aditya-U is exposed to the entire HXR bremsstrahlung emission from the plasma volume, peripheral limiters, and other structural components, which limits its ability to separately study the dynamics of lost and confined runaway electrons (REs). In contrast, the newly developed diagnostic has successfully measured the chord-averaged thin-target HXR bremsstrahlung emission encompassing the core plasma region, particularly within and around the sawtooth inversion radius. The measured HXR spectra are validated through forward modelling code that incorporates plasma parameters, confined RE characteristics, and the geometric configuration of the diagnostic

arXiv:2607.00400v1 Announce Type: new Abstract: Fusion of two epithelial cell sheets brought together in a bilayer configuration is a common step in animal morphogenesis, yet, in contrast to other epithelial fusion processes such as wound healing in a monolayer of cells, it has not been a strong focus of modeling efforts. Here we consider a preliminary stage of bilayer fusion, recently termed "docking." In multiple instances of docking that span apical and basal varieties, cells appear to have a tendency to remodel so as to co-localize their bilateral junctions (match their edges) across the bilayer. Motivated by this observation, we introduce a bilayer cellular Potts model that couples two standard 2D area- and perimeter-elasticity models via short-range, out-of-plane interactions between cell edges. The new coupling involves a single adjustable parameter that minimally models the combined effect of dynamic cytoskeletal protrusions, cadherins, and other potential edge-associated

arXiv:2607.00287v1 Announce Type: new Abstract: Experiments on the DIII-D tokamak have identified a novel regime in which applied resonant magnetic perturbations (RMPs) increase the particle confinement and overall performance. This Letter details a robust range of counter-current rotation over which RMPs cause this density pump-in effect for high confinement (H mode) plasmas. The pump in is shown to be caused by a reduction of the turbulent transport and to be correlated with a change in the sign of the induced neoclassical transport. This novel reversal of the RMP induced transport has the potential to significantly improve reactor relevant, three-dimensional magnetic confinement scenarios.

arXiv:2607.00324v1 Announce Type: new Abstract: This paper studies high-altitude platform (HAP)-assisted sparse cooperative integrated sensing and communication (ISAC) for UAV-enabled ocean monitoring. A fleet of rotary-wing UAVs senses drifting buoys, collects their monitoring data, and reports local posterior estimates to a HAP that performs fusion and sparse cooperation control. The model explicitly accounts for a spatially correlated sea-patch field, patch-aware buoy dynamics, RCS- and clutter-aware echo sensing, fused posterior Cram\'er-Rao bounds (PCRBs), and propulsion-energy-limited UAV mobility. The long-horizon objective is cast as a queue-weighted buffered-collection Markov decision process rather than instantaneous throughput, where each buoy maintains a backlog of buffered observations. The resulting long-horizon design is formulated as a mixed discrete-continuous problem with sensing, communication, mobility, safety, buffered-collection, and onboard-energy constraints.

arXiv:2606.30699v1 Announce Type: cross Abstract: Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning. Current data-driven approaches typically operate on a single dataset, inherently limiting their performance when faced with restricted observations. In practice, multiple datasets are often available for the same physical system, distinguished only by distinct initial conditions or boundary configurations. Here, we present a competitive optimization framework designed to discover shared partial differential equations (PDEs) from multi-source datasets, termed MCO-PDE. The framework first trains independent neural surrogates for each data source, and then employs a soft-competitive weighting mechanism to dynamically assess dataset credibility and aggregate a consensus global coefficient. Integrated with a genetic algorithm for structural search, this approach simultaneously identifies the functional forms and

arXiv:2606.31235v1 Announce Type: new Abstract: Static resonant magnetic perturbations (RMPs) generated by saddle coil current have been applied in J-TEXT tokamak experiments in order to study their effects on tearing mode instabilities. With increasing the RMP amplitude in time during the discharge, the mode stabilization is first observed, but a large locked mode follows if the RMP amplitude is increased to a too large value, indicating that the RMP amplitude is important in determining the plasma response and the tearing mode behavior. By careful adjustment of the RMP amplitude, the (partial) stabilization of the m/n =2/1 tearing mode by RMPs of moderate amplitude has been achieved without causing mode locking (m and n are the poloidal and toroidal mode numbers). To compare with experimental results, nonlinear numerical modeling based on reduced MHD equations has been carried out. With experimental parameters as input, both the mode locking and mode stabilization by RMPs are also

arXiv:2606.31138v1 Announce Type: new Abstract: Two-fluid nonlinear MHD simulations using the TM1 code demonstrate that the formation of magnetic islands at the top and bottom of the H-mode pedestal, together with the strong screening of resonant fields in the gradient region of the pedestal, can account for ELM suppression and density pump-out by n = 2 Resonant Magnetic Perturbations (RMPs) in low-collisionality DIII-D ITER Similar Shape (ISS) plasmas. Using experimentally relevant transport coefficients, neoclassical resistivity, electron collisionality, and RMP amplitudes, nonlinear MHD simulations reproduce the observed level of density reduction (density pump-out) in DIII-D due the formation of narrow magnetic islands and resulting enhanced collisional transport in the resistive foot of pedestal. For large amplitude RMPs (Br/Bt>1*10-4) simulations predict field penetration and pressure reduction at the top of the pedestal consistent with experimental observations at the onset of

arXiv:2606.30978v1 Announce Type: new Abstract: We present an enhanced random-phase-approximation--local-density-approximation (e-RPA-LDA) model for the stopping power of ions that is valid over a wide range of conditions, from cold solids through warm dense matter to high-energy-density plasmas. The electronic stopping is computed from the RPA dielectric response in the local-density approximation over an average-atom electron density obtained in a muffin-tin potential with the Flexible Atomic Code, augmented by four corrections to the earlier RPA-LDA model of Wang et al.: a strong-collision correction for large-momentum-transfer events, a static local-field correction for electron correlations, an electron-binding correction, and the higher-order Barkas and Bloch terms. The resulting proton stopping powers agree with the NIST PSTAR and IAEA databases across the periodic table and for compounds -- providing a physics-based alternative to semi-empirical codes such as SRIM -- and

arXiv:2606.31668v1 Announce Type: new Abstract: Synthetic aperture radar automatic target recognition (SAR ATR) is critical for Earth observation and defense, but its practical deployment is constrained by scarce annotated training data. Self-supervised pre-training alleviates this label bottleneck, yet prevailing Transformer architectures incur prohibitive quadratic computational complexity, and conventional universal masking neglects the unique electromagnetic scattering properties intrinsic to SAR imagery. To address these limitations, we propose SAMBA (Scattering-Guided Bidirectional Mamba), an efficient self-supervised pre-training foundation model for SAR target interpretation. Our framework features three core innovations: (i) a linear-complexity Mamba encoder with a mid-sequence class token to mitigate computational bottlenecks; (ii) a three-level hierarchical Scattering-Guided Masked Autoencoder (SG-MAE) masking strategy guided by SAR physical priors, aligning the pretext

arXiv:2606.31332v1 Announce Type: new Abstract: Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity. Current solvers are often limited to static predictions or require computationally intensive heuristic searches. We present CryoACE, an end-to-end framework that reconstructs precise atomic graphs for both homogeneous and heterogeneous structures. Our method features two key innovations: an atom-centric reconstruction paradigm, where density features are sampled directly at atomic coordinates and iteratively recycled to refine structures, replacing expensive voxel convolutions for efficient multimodal fusion; and a training-free guidance mechanism that leverages predicted local resolution priors to resolve dynamic ambiguity. Validated on a newly constructed high-quality dataset, CryoACE significantly outperforms existing baselines on static benchmarks and, for the first time, unveils

arXiv:2606.30699v1 Announce Type: new Abstract: Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning. Current data-driven approaches typically operate on a single dataset, inherently limiting their performance when faced with restricted observations. In practice, multiple datasets are often available for the same physical system, distinguished only by distinct initial conditions or boundary configurations. Here, we present a competitive optimization framework designed to discover shared partial differential equations (PDEs) from multi-source datasets, termed MCO-PDE. The framework first trains independent neural surrogates for each data source, and then employs a soft-competitive weighting mechanism to dynamically assess dataset credibility and aggregate a consensus global coefficient. Integrated with a genetic algorithm for structural search, this approach simultaneously identifies the functional forms and

arXiv:2606.30646v1 Announce Type: new Abstract: Speech recruits the same executive, attentional, and working memory processes underlying instrumental activities of daily living, or IADLs, providing a non-invasive proxy for cognitive assessment. Yet most speech-based dementia detection systems depend on transcription, discard within-recording temporal structure, and are validated on a single English corpus with known recording artifacts. We propose an ASR-agnostic framework operating directly on Mel spectrograms. Our key contribution is extracting spectrotemporal displacement fields from consecutive spectrogram frames, capturing shifting spectral energy patterns as digital biomarkers of cognitive decline. These features are fused with CNN-ConvGRU acoustic embeddings via a learned cross-attention mechanism and aggregated using a Transformer encoder with learnable query pooling. A composite temporal loss enforces smoothness and contrastive coherence across segments. We train independent

arXiv:2606.28933v1 Announce Type: new Abstract: Venture capital (VC) investment decisions face distinct challenges, such as multi-source heterogeneous data, non-stationary time series, and the demand for explainable predictions in high-stakes, low-data settings. To overcome these issues, we introduce \textbf{FinInvest-GTCN}, a Graph-Temporal-Causal Network that redefines the task from content recommendation to quantitative risk-return assessment. This architecture combines a relational graph encoder to capture the investment ecosystem's topology, a multi-scale temporal fusion module to handle long-term dependencies and non-stationarity, and a causal decision head that generates risk-adjusted predictions with interpretable causal attributions. A core innovation is the Meta-Causal Adaptation (MCA) strategy, which facilitates robust fine-tuning for new, data-scarce sectors by aligning updates with causally-plausible structures derived from meta-pretraining. Comprehensive experiments on

arXiv:2606.28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures. These structures often represent aggregations (e.g., products to categories to regions), where forecasts must not only be accurate but also coherent, meaning that lower-level predictions sum correctly to higher-level forecasts. Traditional statistical methods, such as Bottom-Up and MinT, enforce coherence through post-processing but fail to model complex nonlinear temporal dependencies and covariate interactions. We propose Hierarchical Temporal Fusion (HTF), a novel extension of the Temporal Fusion Transformer (TFT) that integrates structured hierarchical embeddings with a coherence-aware loss function to ensure consistent forecasts across all levels of a hierarchy. Rather than applying reconciliation after forecasting, HTF embeds coherence directly into the training objective. The

arXiv:2606.30402v1 Announce Type: cross Abstract: Molten salts such as FLiBe (2LiF--BeF$_2$) are leading blanket materials for breeding and recovering tritium in fusion reactors. Predicting tritium speciation requires accurate electronic ground-state energies for representative molten-salt clusters, a demanding task for correlated electronic-structure methods. Here we report the first application of heterogeneous quantum--classical computing to tritium binding in FLiBe. Clusters drawn from ab initio molecular dynamics are partitioned by an embedded-wavefunction (EWF) method into atom-centered fragments, and the largest fragments are solved on IBM quantum hardware using extended sample-based quantum diagonalization (ext-SQD). Across nine clusters, the heterogeneous quantum--classical workflow reproduces fragment ground-state energies with agreement to full configuration interaction within 0.7~kcal/mol and a mean absolute deviation of 0.3~kcal/mol. In contrast, fragmented and

arXiv:2606.30155v1 Announce Type: new Abstract: Compressive fluctuations in hot plasma, including acoustic waves and compressible turbulence, increase the rate of fusion reactions. This power amplification comprises hydrodynamic, ``two-temperature,'' and kinetic components, the first resulting from the clumping of hot ions in the peaks of the fluctuations, the second from the unequal heating of ions and electrons as fluctuations dissipate, and the third from the long mean free paths of fast ions near the Gamow peak, which allow these ions to stream across gradients in fluctuating hydrodynamic fields before colliding. In many cases, the increase in fusion power produced by waves exceeds that produced if the wave energy were instead used for heating. Response functions describing the modification to fusion power by compressive fluctuations are obtained in magnetized and unmagnetized fusion plasmas. Comparison to the related shear flow reactivity enhancement effect, a kinetic mechanism

arXiv:2606.29800v1 Announce Type: new Abstract: In 1991 Reiter et al. (1991 Plasma Phys. Control. Fusion 33 1579) considered the onerous exhaust requirements of ITER, and wrote: "The vacuum pumping problem of a fusion reactor will probably require some novel solution". Here we show that a toroidally oriented pump inlet can passively exploit intrinsic neutral flows to reduce back-flow, raise duct pressure, and ultimately improve particle-exhaust performance. Drawing on previous experimental observations and SOLPS-ITER edge-plasma simulations, we consolidate the evidence for a plasma-imprinted, multi-species toroidal neutral "wind" in detached tokamak divertors. We isolate the underlying mechanism in a prototypical divertor private-flux region using a database of two-dimensional direct simulation Monte Carlo (DSMC) calculations. The ordered neutral motion is recovered with a strong toroidal alignment, kilometre-per-second velocities, and persistence up to several centimetres across

arXiv:2606.28613v1 Announce Type: new Abstract: Nonlinear time-dependent computation of macroscale dynamics in stellarators is motivated by laboratory results showing the possibility of robust operation in conditions where magnetohydrodynamic (MHD) modes are linearly unstable. A new formulation of semi-implicit MHD computation for toroidally shaped magnetic confinement systems uses 2D nodal spectral elements over the poloidal plane and Fourier representation over a generalized toroidal angle. Geometric mappings and steady-state (equilibrium) fields are expanded in the same 3D representation as the time-evolved fields to model non-axisymmetric configurations. For accuracy at large timestep, the semi-implicit operator is based on the ideal-MHD energy integral using 3D pressure and magnetic fields. The nodal spectral elements allow numerical convergence through either h-refinement or p- refinement. Our implementation (NIMSTELL) with the continuous H1 expansion of magnetic-field

When a high-intensity laser interacts with plasma, the charged particles typically oscillate back and forth like waves on the ocean. But what if the laser itself could twist like a whirlpool? Researchers have now demonstrated a rotating, spring-shaped laser pulse, opening new possibilities for fusion energy, particle acceleration, astrophysics and beyond.

arXiv:2606.28043v1 Announce Type: new Abstract: This work identifies an inconsistency between plasma operating scenarios and tritium fuel cycle (TFC) requirements, calling for a re-examination of the traditional reactor-led design approach. The key point is simple: in current TFC architectures, fuel puffing must contain tritium. Moscheni et al. (2026 Nucl. Fusion 66 026008) investigated fuel puffing rates in detached operation. Expanding that database, puffing is shown to exceed core fuelling by about an order of magnitude, from present-day tokamaks to next-step stellarators. Though not unknown in the plasma community, TFC models instead assumed core fuelling to dominate. The implications are severe. In recent TFC architectures, direct internal recycling (DIR) is intended to minimise tritium inventory, but assumes near-50:50 D:T composition. This assumption may become self-defeating: a substantial fraction of the puffed fuel must be tritium. Tritium inventory, doubling time, required

arXiv:2606.27450v1 Announce Type: new Abstract: This study examines the mechanisms underlying sawtooth suppression in the High Beta Tokamak-Extended Pulse (HBT-EP) device. It is observed that strong-intensity sawtooth activities correlate with reduced-amplitude MHD edge modes which are identified as $m/n=3/1$ external kink modes (XK), while sawtooth suppression correlates with larger and saturated edge mode amplitudes. To further investigate these correlations, the plasma-wall coupling was manipulated by adjusting the positions of the conducting walls in HBT-EP. It was found that strong sawtooth events occur when the normalized wall radius $b/a$ is within a critical value. This implies that the plasma-wall distance must be sufficiently small to ensure effective stabilization of the edge mode. Even slight differences in major radius result in significantly different discharge styles, categorized as ``sawtoothing discharges'' and ``sawtooth-suppressed discharges'' respectively. Through

arXiv:2606.27108v1 Announce Type: new Abstract: Saturation of the neutron yield with increasing energy of the condenser bank in a Plasma Focus led to the shutdown of PF research focussed on controlled nuclear fusion in the past. We review available models of saturation and develop further the model of Lee S., Applied Phys. Lett. 95, 151503, 2009. This model relies on the well-known and generally accepted model of Lee S., J. Fusion Energy 2014, 33, 319 of Plasma Focus discharges and describes saturation in terms of the dynamic resistance, i.e. the rate of change of PF inductance due to the motion of the plasma sheath during rundown. A model of this sheath discussed in Di Vita A., J. Plasma Physics, 1993, 50, 1 shows that its spontaneous filamentation rules the dynamic resistance, spoiling the power supply from the condenser bank to the plasma at the values of condenser bank energy above 0.5 MJ values which are relevant to a fusion reactor. Together, these two models lead to the

arXiv:2606.26675v1 Announce Type: new Abstract: The magnetohydrodynamic (MHD) equilibrium $\beta$ limits in the Chinese First QuasiAxisymmetric Stellarator (CFQS) are investigated using the NTEC code, for both the standard and the magnetic island configurations. The equilibrium $\beta$ limit is identified upon the onset of the rapid destruction of nested flux surfaces by evaluating several numerical metrics, including the fractal dimension, weighted Birkhoff average, and effective volume of parallel diffusion. In the standard configuration, the net-current-free and the bootstrap-current-carrying equilibria can sustain well-ordered magnetic surfaces up to $\langle\beta\rangle\approx1.5\%$. The proliferation of stochastic field lines starts after the critical overlap between the internal major islands and the high-order island chains. Two types of divertor island configurations are studied based on net-current-free equilibria. It is found that the edge islands may transition into open

arXiv:2606.26513v1 Announce Type: new Abstract: This paper reviews the experimental progress of negative triangularity (NT), a tokamak configuration where the poloidal cross-section is a reversed-D shape compared to the conventional positive triangularity (PT) shape. NT is a promising reactor scenario that addresses the fundamental tension between performance, exhaust, and robustness. NT studies have accelerated globally across these three pillars over the past several years. While tokamak pilot plants are typically designed for the standard PT H-mode regime, this approach faces significant challenges in balancing high core performance with manageable heat and particle exhaust as well as reliable robustness. In contrast, NT plasmas have achieved H-mode-level confinement while remaining robustly free of the deleterious edge localized mode (ELM) instability. Regarding exhaust, NT offers a larger divertor wetted area on the outboard side and demonstrates compatibility with detachment and

arXiv:2606.26317v1 Announce Type: cross Abstract: Accurate fault diagnosis of rolling element bearings in rotating machinery is considered essential for ensuring industrial safety and enabling predictive maintenance. Conventional statistical feature-based methods rely on predefined descriptors, whose diagnostic sensitivity is constrained by fixed configurations and limited adaptability across varying fault conditions. Although deep learning approaches offer strong representational capacity, their effectiveness is often restricted by high data requirements and reduced interpretability. In this work, a parametric adaptive feature extraction framework is proposed, in which feature characteristics are learned directly from data rather than being manually specified. Multiple complementary representations are extracted from vibration signals, including absolute features capturing signal energy distribution, signed moment features reflecting waveform asymmetry, and AC-coupled moment features

arXiv:2606.26520v1 Announce Type: new Abstract: Mammalian cell-culture processes underpin the manufacture of many biopharmaceuticals, yet keeping a run on track is hard: critical process parameters drift over days, and an off-specification trend is often confirmed too late to intervene. Early-stage, multi-day forecasts could enable timely adjustment of feeding, sampling, and control, but bioprocess forecasting is challenging because measurements are sparse and irregularly sampled, operating conditions are heterogeneous across cell lines and media, and runs with near-identical early behaviour can diverge into different futures. We propose an adaptive framework combining a Gated Bottleneck Latent Ordinary Differential Equation (GB-Latent ODE) with Multi-Path Just-In-Time Fine Tuning (MP-JIT-FT). The GB-Latent ODE augments the stan dard Latent ODE with learnable variable-wise gating and a mask-aware bottleneck that compress high-dimensional sparse inputs, improving learning under limited

arXiv:2606.26536v1 Announce Type: new Abstract: In recent years, technological developments and activities by private actors have led a reemerged discussion of the potential of nuclear fusion to meet growing global energy demands. So far, however, fusion technologies remain at comparatively low development levels and their deployment in commercial power plants is probably still decades away. Regardless, over the last decades, many cost studies have been conducted that estimate the future cost of potential fusion power plants. But to date, there is no systematic and harmonized assessment of these projections. Therefore, this study conducts a stochastic analysis of future fusion power plant costs for three distint technology lines, magnetic confinement, inertial confinement, and magneto-inertial confinement fusion, including cost assessments of different technology maturity levels. These levels are further assessed to determine projected learning rates for future fusion costs. For

arXiv:2606.25213v1 Announce Type: new Abstract: A new field solver has been implemented in the global electromagnetic total-$f$ gyrokinetic particle-in-cell code XGC to extend the code's capability to large-scale reduced MHD-type instabilities in tokamak plasma. While XGC's regular field solver is accurate at typical microturbulence scales of the order of the ion Larmor radius in tokamaks with arbitrary aspect ratio, a more accurate field solver is required for large-scale (i.e., low toroidal mode number) MHD-type modes such as internal kink, tearing and peeling modes. The higher accuracy of the new field solver is achieved by dropping the (large aspect ratio) assumption that the poloidal magnetic field is much smaller than the toroidal magnetic field, while its numerical complexity is controlled by using a spectral discretization in the toroidal direction. To cover the entire spectrum from large-scale MHD-type modes to small-scale microturbulence, the regular and the new field solver

arXiv:2606.25167v1 Announce Type: new Abstract: The channel flow of a liquid metal driven by a rapidly varying applied magnetic field is analyzed. The flow configuration, physical properties, and parameters correspond to a duct within a liquid-metal blanket of a nuclear fusion reactor under off-normal plasma conditions, such as plasma disruptions. The problem is solved numerically using a one-dimensional flow approximation. The longitudinal magnetic field, decaying at a typical rate on the order of 100 T/s, induces eddy currents that interact with a steady wall-normal magnetic field, generating the Lorentz force that drives the flow. Standing Alfv\'en waves are identified as the key mechanism controlling the liquid metal's response. These waves manifest as large-amplitude, gradually decaying oscillations of velocity, the induced magnetic field, and eddy currents. A parametric study predicts a severe response developing within the first few milliseconds of the event, with maximum flow

arXiv:2606.25174v1 Announce Type: new Abstract: Field-scale retrieval of surface soil moisture (SM), leaf area index (LAI), and plant height (PH) is essential for precision agriculture, yet it remains an ill-posed inverse problem. Concurrent variations in soil moisture and canopy density generate substantial ambiguities in radar backscatter and spectral responses, which reduces the effectiveness of traditional feedforward regression models in heterogeneous smallholder cropping systems. This study presents the Iterative Energy-Based Transformer (iEBT) for the joint retrieval of coupled soil-canopy states from Sentinel-1 C-band SAR and Sentinel-2 multispectral time series. Instead of direct regression, iEBT embeds multi-modal predictors within a shared sequence, produces an initial state estimate, and iteratively updates the target [SM, LAI, PH] vector through normalized gradient descent to minimize a learned scalar compatibility energy function. Using 700 quality-controlled field

arXiv:2606.24327v1 Announce Type: new Abstract: The Triangular mesh-based gyrokinetic code TRIMEG-C1 solves the gyrokinetic equations using the particle-in-cell scheme to simulate electromagnetic instabilities in tokamak plasmas. TRIMEG-C1 utilizes a high-order C1 finite element method, which captures the accurate physics with lower grid resolution than the C0 method. In this work, we focus on achieving a portable implementation on multiple graphics processing unit (GPU) architectures to accelerate the TRIMEG-C1 code for future physics studies. The OpenMP framework is chosen as the acceleration framework for GPU offloading on different hardware platforms, specifically, NVIDIA and AMD GPUs. The particle pushing procedure, as well as particle-to-grid operations have been adapted for GPU execution. A speedup of $\approx9$ for the particle pusher kernel is achieved on 2 AMD MI300A APUs (Accelerated Processing Unit) compared with 2 AMD 9754 CPUs. In addition, the efficiency of hybrid

arXiv:2606.23974v1 Announce Type: new Abstract: The Lawson Machine 26 (LM26) at General Fusion has demonstrated compressional heating of a spherical tokamak deuterium plasma as it was compressed by an imploding solid lithium liner. Results from the first 11 compression shots on LM26 are presented, the highest-performing of which show more than a 3x increase in $T_e$, a 10x increase in $n_e$, and a 10x increase in $B_{pol}$ within the plasma driven by 3x radial compression. The experimental device and instrumentation are reviewed in detail, followed by observations about the liner trajectory and evolution of plasma properties, including increases in emission of neutrons, X-rays, and visible radiation. Observations from fast-camera images during compression provide context for interpreting the spatial structure of plasma-wall interaction. Overviews of relevant models and analysis are presented. Diagnostic data are used to reconstruct the experimental equilibrium state in computational

arXiv:2606.23953v1 Announce Type: new Abstract: Neutron-producing fusion systems can generate atmospheric carbon-14 when neutrons leak into nitrogen-containing gas. We use MCNP6.2 neutron-transport calculations to estimate the probability that leaked neutrons produce $^{14}$C through $^{14}$N$(n,p)^{14}$C under representative near-ground conditions. For 14.1 MeV deuterium-tritium source neutrons, the conversion probability is 0.25-0.50 across the geometries studied; softer leakage spectra can give larger yields. Scaling this response to a 1 GWe fusion plant shows that percent-level neutron leakage into air would produce an atmospheric $^{14}$C source within a factor of a few of natural global production. At a 2500 GWe fleet scale, limiting fusion-derived radiocarbon to 10% of the natural source implies a mean atmospheric leakage fraction of order $10^{-6}$. These results provide a screening-level source-term estimate for atmospheric $^{14}$C production from terminal neutron leakage in

arXiv:2606.24327v1 Announce Type: cross Abstract: The Triangular mesh-based gyrokinetic code TRIMEG-C1 solves the gyrokinetic equations using the particle-in-cell scheme to simulate electromagnetic instabilities in tokamak plasmas. TRIMEG-C1 utilizes a high-order C1 finite element method, which captures the accurate physics with lower grid resolution than the C0 method. In this work, we focus on achieving a portable implementation on multiple graphics processing unit (GPU) architectures to accelerate the TRIMEG-C1 code for future physics studies. The OpenMP framework is chosen as the acceleration framework for GPU offloading on different hardware platforms, specifically, NVIDIA and AMD GPUs. The particle pushing procedure, as well as particle-to-grid operations have been adapted for GPU execution. A speedup of $\approx9$ for the particle pusher kernel is achieved on 2 AMD MI300A APUs (Accelerated Processing Unit) compared with 2 AMD 9754 CPUs. In addition, the efficiency of hybrid

arXiv:2606.24346v1 Announce Type: new Abstract: Petroleum-engineering search exposes a supervision gap for strong general retrievers: relevant evidence exists in public web text, but domain relevance labels are scarce. To address this gap, we propose PETRA, a large-scale Petroleum Engineering Text for Retrieval Adaptation dataset and pipeline that converts noisy public web data into a curated domain corpus and synthetic supervision for dense retrieval and reranking. PETRA contains 1.36M curated chunks, approximately 2B token equivalents, $\approx$859k, embedding training rows from $\approx$224k anchors, and roughly 400k teacher-scored reranker candidate rows. Its construction combines high-recall energy-domain curation, an energy-domain classifier with 98.4% test accuracy, chunk-grounded query generation, LLM-written hard negatives, and retrieval-mined candidate lists. PETRA improves first-stage in-domain Normalized Discounted Cumulative Gain (nDCG) from 0.703 to 0.763 through score

arXiv:2606.23851v1 Announce Type: new Abstract: This work investigates the implementation of artificial intelligence and machine learning (AI/ML) for real-time monitoring in laser powder bed fusion (LPBF) additive manufacturing. We developed a binary image classification framework for distinguishing normal and abnormal melt pool images using a balanced dataset of 1,200 images collected from Nickel superalloy 625 on the NIST AMMT platform. The study evaluates accuracy and inference time based on control requirements and hardware limitations of open-architecture LPBF machines. We benchmark three transfer learning architectures (ResNet50, EfficientNetB0, and MobileNetV2) against two Random Forest approaches: one trained on EfficientNetB0 feature embeddings (hybrid) and one trained on raw pixel features (baseline). Images are stratified into 80/20 train-test splits, with a further 90/10 validation split on the training set, and undergo standardized resizing, normalization, and

arXiv:2606.23368v1 Announce Type: cross Abstract: Neutrals in the plasma edge are commonly modeled by kinetic equations, with quantities of interest given by macroscopic quantities such as density, velocity, and temperature. In reactor-relevant regimes, fully kinetic descriptions solved by Monte Carlo (MC) methods, although accurate, become computationally expensive, whereas fluid-limit approximations are computationally more efficient but may lose accuracy due to boundary effects or low-collisional regimes. Hybrid fluid-kinetic approaches aim to combine the strengths of both descriptions. However, existing simulation methods face challenges, including interface handling in domain decomposition, unphysical assumptions, and iterative coupling in distribution decomposition. In this work, we propose a distribution-decomposition hybrid model constructed at the particle level based on the kinetic-diffusion Monte Carlo (KDMC) method. The model inherits key properties of KDMC: it is

arXiv:2606.23432v1 Announce Type: new Abstract: The X-Point Target divertor is an alternative divertor configuration with a secondary X-point in its divertor volume. In this work, we investigate the dynamic response and disturbance rejection capacity of the XPT configuration on the TCV tokamak, comparing it to a single null (SN) divertor. We employ a system identification approach using multi-sine perturbations to measure the dynamic response of the detached state in both Ohmic and auxiliary-heated L-mode scenarios upon D$_2$ fuelling, N$_2$ seeding and Electron Resonance Cyclotron Heating (ECRH) power modulations. We demonstrate an inherent disturbance rejection capacity of the XPT at its secondary X-point compared to a SN configuration for all perturbation scenarios. Upstream of its secondary X-point, the dynamic response of the detached state between the XPT and SN appears similar. The disturbance rejection capacity of the XPT could be highly beneficial for passively buffering

arXiv:2606.23427v1 Announce Type: new Abstract: ITER will possess approximately 500 magnetic sensors (mainly measuring poloidal flux) distributed across the first wall. The coils are at known locations but the matching signals not necessarily known. There may also be mistakes in the wiring of the coil polarity. The existing strategy to disambiguate coils uses combinatoric programming of poloidal field coil waveforms of up to 48 discharges of plasma-less operation. An alternate strategy explored in this work is the energisation of a combination of both poloidal and toroidally asymmetric active coils, and Biot-Savart computation of the field solution from all active coils at the sensor coils. A direct brute force permutation of all $N$ coil combinations scales as $O(2^N N!) $ which is intractable for $N>10$. The mathematically formulated optimisation problem was analysed using AI-assisted coding tools, which identified the problem structure as a signed assignment problem and suggested a

arXiv:2606.23386v1 Announce Type: new Abstract: The ITER tokamak project includes a Thomson scattering diagnostic designed to measure electron temperature and density in the plasma core. The system is required to provide measurements over a wide temperature range while meeting stringent accuracy requirements. A previous study analyzed the errors of electron temperature measurements to assess the feasibility of these requirements. The analysis concluded that the central electron temperature could be measured with the required accuracy of 10% for temperatures up to 40 keV at a minimum electron density of 3*10^19 m^-3. However, those results were based on an overestimation of the number of photoelectrons generated in detectors by the scattered radiation due to the incorrect application of the Thomson scattering cross-section. As a consequence, the temperature measurement errors were significantly underestimated. In the present work, the accuracy of electron temperature measurements in

arXiv:2606.23109v1 Announce Type: new Abstract: Ion stopping in dense plasma is crucial for stellar evolution and fusion ignition. However, its behavior in the strong ion-plasma coupling regime beyond the linear limit has long remained elusive, due to formidable experimental challenges. Here we report the first experimental investigation of ion stopping at an unprecedented coupling parameter exceeding unity, achieved by sending laser-accelerated short-pulse and intense quasi-monoenergetic carbon ions ($\sim$583 keV/u, C$^{5+}$) into a uniform, long-lived, well-characterized dense plasma target ($T_e$ $\approx$ 17 eV, $n_e$ $\approx$ 4$\times$10$^{20}$ cm$^{-3}$). By simultaneously measuring ion energy loss and charge-state evolution, we eliminated key experimental ambiguities arising from charge-state determination. Our results clearly show a reduction in stopping power compared with predictions from standard linear dielectric response or binary collision models, and they agree well

arXiv:2606.22989v1 Announce Type: new Abstract: Powder spreading and layer deposition are fundamental stages of Powder Bed Fusion (PBF) technologies and play a critical role in determining process stability and final component quality. This chapter examines the mechanisms governing powder-bed formation, highlighting the interactions between powder characteristics, process parameters, and machine architecture. Particular attention is devoted to the influence of particle size distribution, morphology, cohesion, flowability, layer thickness, recoater velocity, and environmental conditions on powder-bed quality. The resulting powder-bed is discussed as a process state variable whose characteristics, including packing density, surface coverage, effective layer thickness, and spatial homogeneity, directly affect energy absorption, melt-pool stability, defect formation, and mechanical performance. The chapter also reviews the application of the Discrete Element Method (DEM) for modelling

arXiv:2606.22141v1 Announce Type: new Abstract: Resonant wave-particle interactions between externally launched helicon waves (also known as whistler waves) and runaway electrons (REs) have been demonstrated on the DIII-D tokamak. In this work we extend the initial results reported in Choudhury, H. et al. Phys. Rev. Lett. 136, 025101 (2026) by exploring the effects of antenna alignment with the edge magnetic field, toroidal wave propagation direction, and coupled power on RE scattering in the quiescent RE experimental scenario. Two distinct experimental configurations have been investigated: one in which the antenna aligns well with the edge background magnetic field, known as the ideal antenna configuration, and one with misalignment, known as the non-ideal case. Previously, it had been found that helicon power in the ideal antenna configuration prevented RE growth despite the normalized toroidal electric field remaining high enough to drive exponential RE growth in the absence of

As we scour and scorch the Earth for deeper wells of energy, investors and government agencies are pouring billions into nuclear fusion research. The hope is that fusion may ultimately provide a virtually limitless source of clean energy.

arXiv:2606.20097v1 Announce Type: new Abstract: The quadratic complexity of attention poses a critical bottleneck for long-context processing, spurring interest in hybrid attention designs. Most open-source hybrid models adopt a layer-wise strategy. Yet, prior work has noted the inherent difficulty of integrating Linear Attention (LA) with Full Attention (FA), suggesting that the design space of attention hybridization remains underexplored. To probe this space, we conduct interpretability analysis and observe that layers exhibit block-wise functional similarity, while individual heads within the same layer display distinct functional specialization despite sharing input features. This head-level heterogeneity suggests that the head dimension provides a natural and principled granularity for fusing heterogeneous attention signals. Building on this insight, we introduce HydraHead, a novel architecture that hybridizes FA and LA along the head axis. HydraHead features two key

arXiv:2606.19874v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has significantly boosted novel view synthesis and high-fidelity scene reconstruction, expanding the potential of 3DGS-based Visual Simultaneous Localization and Mapping (SLAM) methods. However, most existing systems fail to fully exploit the underlying structural information, which limits rendering quality and often leads to inconsistent maps. To address these limitations, we propose MMD-SLAM, a structure-enhanced Visual SLAM framework that leverages the Atlanta World (AW) assumption to guide a Multi-Meta Gaussian representation for photorealistic mapping. First, we introduce a point-line fusion strategy for pose optimization, where 3D line segments are incorporated to improve tracking robustness and provide additional constraints for mapping. Second, we design a Multi-Meta Gaussian representation with dominant directions, explicitly encoding structural priors from the AW hypothesis. Finally, we propose a

arXiv:2606.19670v1 Announce Type: new Abstract: Extending earlier work in Physics-informed Meta-instrument for eXperiments (PiMiX) [1], PiMiX~2.0 is an artificial-intelligence (AI)-enhanced data-fusion and analysis framework that integrates multi-experiment multi-modal radiographic imaging and tomography (RadIT) with physics-informed reasoning and agentic AI workflows. The framework supports automated data ingestion, multimodal image processing from one or more experiments, three-dimensional (3D) and time-resolved three-dimensional (4D) reconstruction, and physics-aware interpretation of experimental observations. The PiMiX agents are designed for deployment on desktop and laptop systems commonly used in experimental workflows, while remaining scalable to high-performance computing environments for computationally intensive tasks. By coupling RadIT instrumentation and measurements with geometry, physics, computation, and statistical inference, PiMiX 2.0 aims to accelerate RadIT data

arXiv:2606.19523v1 Announce Type: new Abstract: Modern stellarators are typically designed by optimizing the shape of the plasma boundary surface, with the parameters taken to be Fourier amplitudes. Many promising optimization algorithms such as Bayesian methods require bound constraints on the parameters and are most efficient when each parameter is scaled similarly to the others. With the typical Fourier parameterization, it is unclear how to set these bounds: wide constraints lead to self-intersecting boundaries and frequent failures of the MHD equilibrium calculation, while tight bound constraints limit expressiveness. To address these issues, here we propose two new parameter spaces for stellarator optimization. Both begin with a dataset of existing stellarator boundaries. In the first approach, a quantile transformation is applied to each Fourier degree of freedom, mapping the data distribution to a uniform distribution on the unit interval. In the second approach, principal

arXiv:2606.19269v1 Announce Type: new Abstract: Training-free anomalous sound detection (ASD) scores a test clip against a memory bank of normal embeddings from a frozen pretrained audio encoder. Recent work attributes domain-shift robustness mainly to how frame-level features are pooled over time; the scoring backend applied on top of the pooled embedding has received far less systematic attention. Using a single frozen BEATs encoder on the DCASE 2023 Task 2 development set (all seven machine types), we cross four classical backends -- nearest-neighbor cosine distance, Mahalanobis distance, locally density-normalized kNN, and PCA-subspace reconstruction residual -- with three temporal poolings (mean, GeM, max). Switching the backend moves target-domain AUC by 13.8 points on average (up to 53.8), whereas switching the pooling moves it by only 3.2 points: in this training-free regime, the backend, not the pooling, dominates domain-shift robustness. No backend wins everywhere, but the

arXiv:2606.18078v1 Announce Type: new Abstract: Geostatistical spatial prediction for environmental processes is typically undertaken using Gaussian process models via Kriging, while machine learning (ML) algorithms are state-of-the-art for non-spatial prediction. An exciting recent fusion of these ideas imbibes traditional ML algorithms with the capacity to deal with spatial autocorrelation, leading to improved predictive performance. A range of approaches have been proposed, including fusion with Gaussian processes, observation-driven correlation structures, spatial basis functions and local geographical fitting. However, there has been no numerical comparison of their relative predictive performances, which is needed to advise environmental scientists on the optimal approach to use. This paper fills this knowledge gap, and focuses on random forests as the ML algorithm because they are more computationally and conceptually straightforward to implement than deep learning algorithms.

arXiv:2606.18029v1 Announce Type: new Abstract: The optimised tokamak-stellarator hybrid concept (Henneberg and Plunk 2024) has the potential to combine tokamak and stellarator advantages to achieve magnetically confined fusion. These compact quasi-axisymmetric designs can have a low aspect ratio and large plasma volume, good particle confinement, and relatively simple coils. Previous work showed that such magnetic configurations can in principle be reproduced by a single type of non-planar "banana coil" alongside the conventional tokamak coilset (Henneberg and Plunk 2025). In this work, we optimise banana coils while also considering engineering constraints beyond simple geometric measures. We quantify the characteristic geometries of force-optimised banana coils and the magnetic fields they generate, and analyse the mechanisms by which forces may be reduced through optimisation.

arXiv:2606.17733v1 Announce Type: new Abstract: Particle-in-cell (PIC) simulations are widely used for kinetic plasma modeling in energy applications, but their efficiency is often limited by repeated field solves on dense meshes. This work proposes a Latent Residual-Closure Fourier Neural Operator (LRC-FNO) for robust surrogate multi-field solving in PIC simulations. Rather than treating field prediction as a purely data-driven regression task, LRC-FNO formulates PIC field solving as a two-level residual-closure problem involving source compression and source-to-field operator mapping. An autoencoder extracts compact representations of particle-deposited source fields, while a Latent Closure Refiner recovers unresolved residual structures lost during compression. A Coarse-FNO Solver captures the dominant field response, and a Residual-Closure FNO restores full-resolution corrections. The method is tested on three benchmarks with increasing complexity: 1D linear Landau damping (LLD),

arXiv:2606.17375v1 Announce Type: new Abstract: Boron pebble aggregate was tested for the first time as a high-heat-flux granular plasma-facing material in a tokamak divertor. Exposures of up to $q_{\parallel} = \SI{80}{\MW\per\m\squared}$ incident heat flux were conducted in the DIII-D tokamak. Single protruding rods of pebble aggregate composed of sintered amorphous boron pebbles bound with carbon binder were mounted in the Divertor Material Evaluation System (DiMES) sample holders and exposed to L-mode lower single null (LSN) plasmas. Under these heat loads, significant boron dust emission from the boron spheres was observed, and this dust dominates the divertor boron ionization source. Only about half of the released boron was recovered locally as mm-sized particles; with the rest presumably lost mainly as dust into the plasma and vacuum chamber. Preliminary estimates suggest that the rate of surface recession of $\sim$1 cm/s in the pebble conglomerate within the plasma divertor

arXiv:2606.16550v1 Announce Type: cross Abstract: Cryogenic hydrogen isotope fuel layers with high structural integrity and atomic-scale smoothness are prerequisites for symmetric implosion and ignition in inertial confinement fusion (ICF). Using deuterium (D$_2$) as model fuel, we perform large-scale molecular dynamics simulations with a Feynman-Hibbs corrected Silvera-Goldman potential to describe nuclear quantum effects at low temperatures, systematically investigating D$_2$ crystallization inside spherical ablator capsules. By varying substrate lattice constant from 3.1 angstrom to 3.9 angstrom, we demonstrate that lattice matching dictates the transition from coherent epitaxial growth to polycrystalline formation, establishing it as the primary design principle for high-performance targets. When the substrate lattice closely matches the equilibrium hexagonal-close-packed (HCP) spacing of cryogenic D$_2$ (approximately 3.5 angstrom), D$_2$ forms coherent layer-by-layer epitaxial