mahindrautela / BOPINNLinks
Bayesian optimized physics-informed neural network for parameter estimation
☆32Updated 8 months ago
Alternatives and similar repositories for BOPINN
Users that are interested in BOPINN 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…☆74Updated 3 years ago
- Introduction to JAX Workshop @ ETH Zurich, 25 June 2024☆36Updated 3 months ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- ☆97Updated 3 years ago
- Physics Informed Neural Networks (PINNs) + SPINNs + HyperPINNs + Adaptative Loss Weights with JAX 📓 Check out our various notebooks to g…☆34Updated last week
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆41Updated 2 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆69Updated 5 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…☆20Updated 9 months ago
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics☆63Updated 3 years ago
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆18Updated last year
- ☆23Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆50Updated 3 years ago
- ☆116Updated 6 years ago
- ☆54Updated 2 years ago
- Example problems in Physics informed neural network in JAX☆80Updated last year
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 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
- Multi Fidelity Monte Carlo☆24Updated 5 years ago
- ATHENA: Advanced Techniques for High dimensional parameter spaces to Enhance Numerical Analysis☆54Updated 2 years ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- Interpretable machine learning (symbolic regression) using Genetic programming/Gene expression programming and Sparse regression used …☆33Updated 4 years ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated last month
- ☆37Updated last year
- Original implementation of fast PINN optimization with RBA weights☆57Updated 3 months ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆63Updated 5 years ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago
- Tensoflow 2 implementation of physics informed deep learning.☆27Updated 4 years ago
- ☆63Updated 6 years ago
- ☆32Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆73Updated 2 years ago