There are 9 repositories under neural-ode topic.
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations
18.S096 - Applications of Scientific Machine Learning
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Arrays with arbitrarily nested named components.
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Code for the paper "Learning Differential Equations that are Easy to Solve"
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML
Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
Regularized Neural ODEs (RNODE)
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
Official PyTorch implementation for the paper Minimizing Trajectory Curvature of ODE-based Generative Models, ICML 2023
Implementation of (2018) Neural Ordinary Differential Equations on Keras
Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
Latent Differential Equations models in Julia.
Code for our RSS'21 paper: "Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control"