Use deep learning methods to solve PDEs.
Physics Informed Deep Learning (Part I): Data-driven Discovery of Nonlinear Partial Differential Equations.
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.
VPINNs:Variational Physics-Informed Neural Networks For Solving Partial Differential Equations.
hp-VPINNs: Variational physics-informed neural networks with domain decomposition.
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems.
Extended physics-informed neural networks (XPINNs): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets
no paper
Improved architectures and training algorithms for deep operator networks
A physics-informed variational DeepONet for predicting the crack path in brittle materials
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems.
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems.