jtoleary / SPINODELinks
Stochastic Physics-Informed Neural Ordinary Differential Equations
☆17Updated 3 years ago
Alternatives and similar repositories for SPINODE
Users that are interested in SPINODE are comparing it to the libraries listed below
Sorting:
- 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
- ☆14Updated 3 years ago
- Solving High Dimensional Partial Differential Equations with Deep Neural Networks☆34Updated 3 years ago
- ☆14Updated 2 years ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆26Updated 3 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆74Updated 3 years ago
- Neural Galerkin☆16Updated last year
- ☆16Updated last year
- 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
- Simple demo on implementing data driven and physics informed Deep O Nets in pytorch☆15Updated last year
- Hands-on tutorial for implementing Physics Informed Neural Networks in Pytorch☆43Updated 3 months 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
- Deep learning assisted dynamic mode decomposition☆20Updated 3 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆63Updated 5 years ago
- ☆21Updated 4 years ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆26Updated 3 years ago
- Basic implementation of physics-informed neural networks for solving differential equations☆92Updated 7 months ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆30Updated 3 years ago
- Codes associated with the manuscript titled "Multi-stage neural networks: Function approximator of machine precision"☆46Updated last year
- Competitive Physics Informed Networks☆31Updated 10 months ago
- Code of my master's thesis on "Physics Informed Machine Learning of Nonlinear Partial Differential Equations"☆9Updated 4 years ago
- ☆36Updated last year
- Original implementation of fast PINN optimization with RBA weights☆57Updated 3 months ago
- Learning two-phase microstructure evolution using neural operators and autoencoder architectures☆23Updated last year
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆18Updated last year
- Stochastic Physics-Informed Neural Networks: A Moment-Matching Framework for Learning Hidden Physics within Stochastic Differential Equat…☆14Updated 3 years ago
- mathLab mirror of Python Dynamic Mode Decomposition☆99Updated 5 months ago
- SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations☆21Updated 2 years ago