neural-differential-equations

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

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)

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable.

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.

Code for the paper "Learning Differential Equations that are Easy to Solve"

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

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)

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)

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

This repository contains code released by DiffEqML Research

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)

A 30-minute showcase on the how and the why of neural differential equations.

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. https://song921012.github.io/MathEpiDeepLearning/

Code for "Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations"

Using DiffEqFlux to learn underlying differential equations from data.

Codes for paper "Estimating time-varying reproduction number by deep learning techniques"

Tutorials on math epidemiology and epidemiology informed deep learning methods