Papers & Surveys

Electromagnetics

Proposes a 3D FDTD framework using Quantized Tensor Trains that reduces memory and computational costs from conventional volumetric scaling to logarithmic scaling, enabling high-resolution electromagnetic scattering simulations with limited resources.

ScientificML

A 36M-parameter UNet-based PDE solver v2 by Los Alamos National Laboratory, designed for surrogate modeling of partial differential equations in scientific computing.

ScientificML

Proposes a nearly-instant latent space Bayesian optimization method that replaces expensive sequential decision-making with sampling overhead, making BO practical for generative model-driven de novo design pipelines with cheap virtual screens.

Quantum/Chemistry

A physics-informed dual-state query learning framework for GPCR ligand bioactivity prediction, addressing the neglect of dynamic conformational equilibria in existing deep learning approaches to improve functional efficacy discrimination.

ScientificML

Proposes a variational latent Markov operator framework that encodes physical states as latent distributions and evolves them through probabilistic transitions, addressing error accumulation and distribution shift in neural PDE solvers for long-horizon prediction.

CFD

This paper develops a structure-informed data-driven ROM for transport-dominated systems governed by scalar hyperbolic conservation laws, using the kinetic defect formulation to separate characteristic transport from entropy defects localized on shock manifolds.

Electromagnetics

Proposes two ML-based calibration methods to correct electromagnetic shower feature discrepancies between Monte Carlo simulations and real collision data in the CMS calorimeter.

Geophysics

Proposes a three-step framework for ensemble smoother with multiple data assimilation (ES-MDA), unifying existing adaptive variants to balance accuracy and computational cost in reservoir engineering inverse problems.

Electromagnetics

Proposes Neural Modal Decomposition (NMD) to decompose multi-port system response matrices into pole-residue form for neural network-based modeling of RF cavities and photonic devices.

CFD/Fluid Dynamics

VIOT is a generative neural operator for incompressible density transport that predicts divergence-free velocity fields via feed-forward inference, replacing hour-scale optimization in adjoint fluid solvers and enabling rapid transport trajectory generation.

Electromagnetics

This study assesses RF-EMF exposure across all floors of a 20-story building adjacent to a multi-operator shared base station, revealing peak levels of 31.86 V/m near the center of the antenna's vertical coverage zone.

Quantum/Chemistry

A spectral co-design framework that directly couples fabrication-constrained SPSA-Adam optimizer with full-wave finite-element eigenmode simulations for multifrequency Rydberg electrometry, operating without analytical or adjoint gradients.

Electromagnetics

Presents a 3D multiphysics FEM framework for high-frequency induction tube welding, coupling time-harmonic magnetic scalar potential with quasi-stationary heat transport for coupled electromagnetic-thermal simulation.

Electromagnetics

Proposes a hybrid CNN-Transformer architecture for solar flare prediction based on the physical process of magnetic energy release in the solar atmosphere, enabling early warning for C/M/X-class flares.

Electromagnetics

Introduces a reformulation of the S-matrix approach for layered photonic structures, defining the S-matrix as an intrinsic layer property independent of neighboring interfaces to enhance numerical stability.

Electromagnetics

The Anisotropic Maxwell Neural Operator enables rapid parametric modeling of ion cyclotron resonance heating full-wave responses for the EAST tokamak by learning the one-parameter dielectric-field family within a single operator.

Geophysics

A continually learning neural-operator surrogate model replaces the traditional forward solver for efficient Bayesian inversion of three-dimensional time-domain airborne electromagnetic data, addressing prohibitive computational costs.

Electromagnetics

A physics-informed neural operator learns the map between function spaces in magnetohydrodynamic simulations, enabling efficient state inference for magnetic reconnection at any parameter and time step, significantly reducing computational cost at high Lundquist numbers.

ScientificML

Proposes the Kuramoto Neural Operator, introducing coupled oscillator dynamics into operator learning to overcome limitations of fixed-basis architectures for physical phenomena governed by local interactions.

ScientificML

Proposes ADEx-FNO, a framework that extends Fourier neural operators to varying geometries and independent discretizations via ambient-domain embedding and signed distance functions, without modifying core Fourier-operator layers.