An implementation of Neural ODEs in PyTorch.

Alternative method of time-discretization for Neural ODEs

Repository of my Master Thesis Project at TUM at the end of my Ecole Polytechnique's studies. It tackles the subject of "Continuous Motion Interpolation with Neural Differential Equations"

Lagrangian and Hamiltonian Neural Ordinary Differential Equations (NODEs)

PINEURODEs is a repository collecting CMS group research work on the application of neural (stochastic/ordinary) differential equations and physically-informed neural networks to model complex multiscale systems.

Accompanying code for the paper "Amortized reparametrization: efficient and scalable variational inference for latent SDEs

On the forward invariance of Neural ODEs: performance guarantees for policy learning

This repo is the official implementation for the series of works on (Path-dependent) Neural Jump ODEs.

Python tools for non-intrusive reduced order modeling

Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (NeurIPS 2022)

Neural Ordinary Differential Equations for Reinforcement Learning

Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"

[TKDE 2022] The official PyTorch implementation of the paper "Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs".

Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)