The paper introduces the EXTRA homogenization method, which enables accurate and computationally efficient 3D magneto-thermal finite-element simulation of large-scale HTS magnets by selectively resolv…

This paper investigates the limitations of polyconvex constitutive modeling, showing that while theoretically appealing, it can impose overly restrictive constraints and perform poorly in reproducing…

The paper applies a novel hybrid mixed domain decomposition (HMDD) method to solve the complex rotor-stator coupling problem in permanent magnet synchronous machines, enabling rigorous error analysis…

The paper proposes MITL, an MsFEM-inspired transfer learning strategy for CNN-based reduced-order models, enabling efficient and adaptable approximation of multiscale systems with minimal retraining.

The paper introduces Cellular Sheaf Neural Operators, a discretization-aware framework that models constrained PDEs by representing physical states on oriented cell complexes to enforce structure-pres…

The paper proposes a lumped RC equivalent circuit model to accurately simulate the electrical behavior of head tissues in the sub-MHz frequency range, offering a computationally efficient alternative…

The paper proposes using a Physics-Informed Neural Network (PINN) residual as an efficient, physics-guided indicator to guide adaptive mesh refinement (AMR) for classical finite-difference PDE solvers…

The paper proposes a scalable, market-analysis-driven methodology to assess national charging station cybersecurity by extrapolating field test results from a manageable subset of stations to estimate…

The paper introduces GenAI-FDIA, a comprehensive framework that benchmarks various physics-informed generative models to synthesize high-fidelity False Data Injection Attacks (FDIA) for power systems,…

This paper analyzes the performance and explainability requirements of evolutionary algorithms when applied to complex, real-world physics-informed optimization problems, identifying a gap between cur…

The authors demonstrate that a physics foundation model, finetuned on simulation data, can successfully predict complex laboratory fluid dynamics, specifically resolving a long-standing discrepancy in…

The paper proposes a graph attention-based virtual metrology framework that accurately predicts film thickness in semiconductor deposition by modeling structured, directional dependencies among hetero…

The paper introduces a compact, dispersive RC circuit model for electro-quasi-static (EQS) head modeling, accurately representing the brain, skull, and scalp layers for brain-oriented applications.

VFEAgent is a novel multi-agent framework that automates the entire Finite Element Analysis (FEA) workflow, achieving high success rates in generating complete and physically valid simulations directl…

The paper introduces a physics-informed active learning framework to optimize GaN tri-gate FinFETs for vertical power delivery, identifying a multi-fin device (D1) that significantly outperforms a sin…

The paper proposes using pseudo-sensitivities, derived from adjoint sensitivity fields, as an optimal conditioning signal in a Bernoulli flow-matching framework to significantly improve the out-of-dis…

The paper demonstrates that enforcing a local conservative finite volume structure is crucial for achieving stable, accurate long-term autoregressive rollouts of plasma transport simulations, outperfo…

The paper systematically characterizes the fault response of the Intel NCS2 accelerator to electromagnetic fault injection, revealing a major degradation mode that is undetectable by standard inferenc…

This paper investigates the thermal constraints of deploying AI compute infrastructure in space, comparing GPUs and compute-in-memory (CIM) accelerators using a co-design methodology.

The paper introduces a non-intrusive variant of index-aware learning for solving differential-algebraic equations (DAEs), ensuring that learned solutions maintain physical consistency like Kirchhoff's…