matlab-deep-learning / fourier-neural-operatorLinks
☆19Updated 2 years ago
Alternatives and similar repositories for fourier-neural-operator
Users that are interested in fourier-neural-operator are comparing it to the libraries listed below
Sorting:
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆75Updated 3 years ago
- PECANNs: Physics and Equality Constrained Artificial Neural Networks☆23Updated 2 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆31Updated 3 years ago
- Boosting the training of physics informed neural networks with transfer learning☆26Updated 4 years ago
- Pytorch implementation of Bayesian physics-informed neural networks☆61Updated 3 years ago
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆25Updated 7 months ago
- Physics-Informed Neural Network (PINN) for Solving Coupled PDEs Governing Thermochemical Physics in Bi-Material Systems☆56Updated 4 years ago
- Implementing a physics-informed DeepONet from scratch☆46Updated 2 years ago
- We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.☆92Updated 2 years ago
- Enhancing PINNs for Solving PDEs via Adaptive Collocation Point Movement and Adaptive Loss Weighting☆37Updated 2 years ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆26Updated 2 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆50Updated 3 years ago
- ☆83Updated last year
- Physics Informed Neural Network (PINN) for Burgers' equation.☆71Updated last year
- ☆23Updated 3 years ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆49Updated 2 years ago
- Research/development of physics-informed neural networks for dynamic systems☆27Updated 9 months ago
- Sparse Physics-based and Interpretable Neural Networks☆51Updated 3 years ago
- Original implementation of fast PINN optimization with RBA weights☆58Updated 4 months ago
- Physcial Informed Extreme Learning Machine(PIELM) method to solve PDEs, such as Possion problem☆13Updated 8 months ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆29Updated last year
- Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN), a framework that extends Physics-Informed Neural Network…☆16Updated last year
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆152Updated last year
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆19Updated last year
- POD-PINN code and manuscript☆52Updated 9 months ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- Basic implementation of physics-informed neural network with pytorch.☆75Updated 2 years ago
- Competitive Physics Informed Networks☆31Updated 11 months ago
- An implementation of Physics-Informed Neural Networks (PINNs) to solve various forward and inverse problems for the 1 dimensional wave eq…☆40Updated 2 years ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago