ASEM000 / Physics-informed-neural-network-in-JAXLinks
Example problems in Physics informed neural network in JAX
☆82Updated 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:
- ☆105Updated 4 years ago
- Applications of PINOs☆142Updated 3 years ago
- PINNs-JAX, Physics-informed Neural Networks (PINNs) implemented in JAX.☆57Updated last year
- ☆230Updated 4 years ago
- Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed c…☆116Updated 3 years ago
- ☆42Updated 5 years ago
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆158Updated 10 months ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆81Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- Pseudospectral Kolmogorov Flow Solver☆41Updated 2 years ago
- This is the repository for the code used in the ICML23 paper called "Achieving High Accuracy with PINNs via Energy Natural Gradient Desce…☆25Updated last year
- Solving PDEs with NNs☆55Updated 2 years ago
- ☆54Updated 3 years ago
- ☆28Updated last year
- Gaussian process-based interpretable latent space dynamics identification through deep autoencoder☆35Updated 2 weeks ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆88Updated 4 years ago
- hPINN: Physics-informed neural networks with hard constraints☆149Updated 4 years ago
- ☆116Updated 10 months ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 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
- Code for the paper "Thermodynamics-informed graph neural networks" published in IEEE Transactions on Artificial Intelligence (TAI).☆105Updated last year
- An RL-Gym for Challenge Problems in Data-Driven Modeling and Control of Fluid Dynamics.☆87Updated 4 months ago
- ☆157Updated 3 years ago
- ☆117Updated 6 years ago
- ☆183Updated last year
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆85Updated 3 months ago
- Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.☆75Updated last month
- ☆54Updated 2 years ago
- ☆197Updated 8 months ago