Efficient and stable SAV-based methods for gradient flows arising from deep learning

Inferring biophysical properties of membranes during endocytosis using machine learning

Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions

DeepStSNet: Reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving partial differential equations with sharp solutions

Analysis and Hermite Spectral Approximation of Diffusive-Viscous Wave Equations in Unbounded Domains Arising in Geophysics

Deepstsnet: Reconstructing the Quantum State-Resolved Thermochemical Nonequilibrium Flowfield Using Deep Neural Operator Learning With Scarce Data

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions

Analysis and Hermite spectral approximation of diffusive-viscous wave equations in unbounded domains arising in geophysics

High Order Conservative Finite Difference/Fourier Spectral Methods for Inviscid Surface Quasi-Geostrophic Flows

Physics-informed neural networks for inverse problems in supersonic flows

A spectral method for stochastic fractional PDEs using dynamically-orthogonal/bi-orthogonal decomposition

Learning functional priors and posteriors from data and physics

A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

Physics-Informed Neural Networks for Inverse Problems in Supersonic Flows

Physics-informed neural networks (PINNs) for fluid mechanics: a review

DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators