Papers & Surveys

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 surrogate is developed for 2D magnetohydrodynamic magnetic reconnection, learning mappings between function spaces to generate states across parameters and times without repeatedly solving expensive resistive-MHD simulations.

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.

Framework Paper / Neural Operator

NVIDIA's PINN framework built on neural operators (FNO/DeepONet/AFNO) for end-to-end differentiable physics simulation, with multi-GPU training and deployment.

Annual Survey / Physics-Informed Learning

A systematic review of 2025-2026 progress in physics-informed ML - neural operators, diffusion for PDE solving, large benchmarks and industrial deployment.

CFD

Proposes PITT methodology combining symbolic equation tokenization with Fourier neural operator encoder to solve nonlinear hyperbolic balance laws for relativistic Schwarzschild-Burgers fluid flows with shock waves.

ScientificML

Proposes Graph Wavelet Compressed Sensing (GWCS) for offline compression of graph signals via sparse wavelet-domain representations, reducing data preparation costs for scientific machine learning training.

ScientificML

Proposes a collaborative framework of AI virtual labs under a citation-based influence economy to automate neural operator architecture search and training evaluation.

CFD

Proposes an improved Fourier Neural Operator for modeling 2D Rayleigh-Bénard convection by predicting time increments, achieving a compact model (314k parameters, 7ms inference) while revealing accuracy limitations imposed by training data resolution.

ScientificML

PRONE introduces a Petrov-Galerkin regression framework Ψ(X)K≈Φ(Y) that unifies DMD, SINDy, Koopman regression, and sparse regression as variants of a single construction for data-driven dynamics learning.

ScientificML

Proposes an adaptive memory gating mechanism enabling neural operators to dynamically adjust memory weights based on observation conditions, improving adaptability in solving time-dependent PDEs.

Electromagnetics

Proposes a KAN-based single-parameterized surrogate model to accelerate drift kinetic equation solving for fast neoclassical toroidal viscosity torque calculation in tokamak plasma modeling.

CFD

Proposes a geometry-aware anisotropic boundary correction method using neural operators as an efficient alternative to traditional CFD solvers for aerodynamic simulation, improving physical modeling near solid boundaries.

ScientificML

Proposes a method to enforce hard physical constraints in generative models for semiconductor manufacturing by construction, ensuring that generated masks, layouts, and process recipes strictly comply with lithography, transport, reaction, and device-physics constraints.

ScientificML

GENERIC-FNO embeds the metriplectic degeneracy structure of nonequilibrium thermodynamics into Fourier neural operators, coupling reversible energy-conserving and irreversible entropy-producing dynamics via degeneracy conditions, extending structure-preserving neural operators beyond single conservation laws.

CFD

Proposes a physics-guided hybrid architecture combining UNet and FNO, integrating multi-modal Sentinel-1/2 remote sensing with shallow water equation constraints for end-to-end flood prediction.