There are 9 repositories under neural-differential-equations topic.
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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.
Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
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"
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
Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)
This repository contains code released by DiffEqML Research
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
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)
Awesome-spatial-temporal-data-mining-packages. Julia and Python resources on spatial and temporal data mining. Mathematical epidemiology as an application. Most about package information. Data Sources Links and Epidemic Repos are also included. Keep updating.
A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (NeurIPS 2022)
A 30-minute showcase on the how and the why of neural differential equations.
Repository for my master thesis at EPFL: "Neural controlled differential equations for crop classification"
Sampling from the solution of the Zakai equation, using the Signature and Conditional Wasserstein GANs
Using DiffEqFlux to learn underlying differential equations from data.
Tutorials on math epidemiology and epidemiology informed deep learning methods
Code for "Controlled Differential Equations on Long Sequences via Non-standard Wavelets" paper. ICML23
Understanding the idea, intuition and implementation of Neural Differential Equations. Clearly explained and fully commented.
Codes for paper "Estimating time-varying reproduction number by deep learning techniques"
A dynamical systems approach to adaptive patch foraging by using Neural Differential Equations.