ASEM000 / Physics-informed-neural-network-in-JAXLinks
Example problems in Physics informed neural network in JAX
☆81Updated 2 years ago
Alternatives and similar repositories for Physics-informed-neural-network-in-JAX
Users that are interested in Physics-informed-neural-network-in-JAX are comparing it to the libraries listed below
Sorting:
- ☆110Updated 4 years ago
- PINNs-JAX, Physics-informed Neural Networks (PINNs) implemented in JAX.☆58Updated last year
- Applications of PINOs☆146Updated 3 years ago
- Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed c…☆117Updated 3 years ago
- hPINN: Physics-informed neural networks with hard constraints☆153Updated 4 years ago
- Gaussian process-based interpretable latent space dynamics identification through deep autoencoder☆38Updated 2 weeks ago
- ☆42Updated 5 years ago
- ☆117Updated 11 months ago
- This is the repository for the code used in the ICML23 paper called "Achieving High Accuracy with PINNs via Energy Natural Gradient Desce…☆27Updated last year
- ☆241Updated 4 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆241Updated 3 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Updated 5 years ago
- An RL-Gym for Challenge Problems in Data-Driven Modeling and Control of Fluid Dynamics.☆93Updated 2 weeks ago
- ☆199Updated last year
- ☆118Updated 6 years ago
- ☆54Updated 3 years ago
- Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.☆78Updated 2 weeks ago
- Deep learning library for solving differential equations on top of PyTorch.☆62Updated 5 years ago
- This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means…☆181Updated 4 years ago
- ODIL (Optimizing a Discrete Loss) is a Python framework for solving inverse and data assimilation problems for partial differential equat…☆134Updated 2 months ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆84Updated 3 years ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆39Updated 2 years ago
- ☆168Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆159Updated last year
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆50Updated 5 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆87Updated 5 months ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆94Updated 2 years ago
- Code for the paper "Thermodynamics-informed graph neural networks" published in IEEE Transactions on Artificial Intelligence (TAI).☆106Updated last year