GitTeaching / Predicting-using-Neural-ODELinks
Deep Learning - Predicting using Neural Ordinary Differential Equations - torchdiffeq.
☆15Updated 4 years ago
Alternatives and similar repositories for Predicting-using-Neural-ODE
Users that are interested in Predicting-using-Neural-ODE are comparing it to the libraries listed below
Sorting:
- This repository contains code for the paper "MAgNet: Mesh-Agnostic Neural PDE Solver" https://arxiv.org/abs/2210.05495☆37Updated 2 years ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆27Updated 4 years ago
- Model hub for all your DiffeqML needs. Pretrained weights, modules, and basic inference infrastructure☆27Updated 2 years ago
- ☆15Updated last year
- ☆13Updated 4 years ago
- ☆23Updated 4 months ago
- Deeplearning project at The Technological University of Denmark (DTU) about Neural ODEs for finding dynamics in ordinary differential equ…☆17Updated 3 years ago
- ☆35Updated 2 years ago
- Experiment with Neural ODE on Pytorch☆14Updated 6 years ago
- About Code release for “RoPINN: Region Optimized Physics-Informed Neural Networks” (NeurIPS 2024), https://arxiv.org/abs/2405.14369☆58Updated 3 months ago
- [AAAI24] LE-PDE-UQ endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both…☆16Updated last year
- Official PyTorch implementation of "Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics" (https://openreview.net/forum…☆52Updated 5 years ago
- Learning with Higher Expressive Power than Neural Networks (On Learning PDEs)☆16Updated 4 years ago
- [ICLR 2024] Scaling physics-informed hard constraints with mixture-of-experts.☆35Updated last year
- Predicting wave propagation on shallow water with deep neural networks☆22Updated 2 years ago
- The public repository about our joint FINN research project☆38Updated 3 years ago
- Differentiable Physics-informed Graph Networks☆68Updated 5 years ago
- 🏔️ PINNACLE: PINN Adaptive ColLocation and Experimental points selection☆24Updated last year
- Neural Stochastic PDEs: resolution-invariant modelling of continuous spatiotemporal dynamics☆53Updated 2 years ago
- Consistent Koopman Autoencoders☆74Updated 2 years ago
- ☆20Updated 11 months ago
- 🌌 Applications of Physics-Informed ML: A collection of notebooks from my Masters research, exploring how machine learning can solve scie…☆11Updated 11 months ago
- ☆11Updated 4 years ago
- Long-term probabilistic forecasting of quasiperiodic phenomena using Koopman theory☆35Updated 3 years ago
- Training neural networks to disentangle conservative and dissipative dynamics☆10Updated 3 years ago
- Pytorch Implmentation of Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series (ACSSM), ICLR 2025 Oral☆18Updated 7 months ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago
- Synthetic Lagrangian Turbulence by Generative Diffusion Models☆26Updated 11 months ago
- Official code for AL-PINNS: Augmented Lagrangian relaxation method for Physics-Informed Neural Networks☆11Updated 2 years ago
- Code for the ICLR 2020 paper "Learning to Control PDEs"☆35Updated 5 years ago