Zero coordinate shift: whetted automatic differentiation for physics-informed operator learning
From MaRDI portal
Publication:6497254
DOI10.1016/J.JCP.2024.112904MaRDI QIDQ6497254
Kuangdai Leng, Mallikarjun Shankar, Jeyan Thiyagalingam
Publication date: 6 May 2024
Published in: Journal of Computational Physics (Search for Journal in Brave)
Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical methods for partial differential equations, boundary value problems (65Nxx)
Cites Work
- Unnamed Item
- Nesting forward-mode AD in a functional framework
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
- Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
- A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
- CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method
- Scientific machine learning through physics-informed neural networks: where we are and what's next
- Physics-informed neural networks for high-speed flows
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations
- Neural‐network‐based approximations for solving partial differential equations
- New development in freefem++
- A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
- Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
- fPINNs: Fractional Physics-Informed Neural Networks
- JAX-DIPS: neural bootstrapping of finite discretization methods and application to elliptic problems with discontinuities
- Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
This page was built for publication: Zero coordinate shift: whetted automatic differentiation for physics-informed operator learning