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.

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

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

Linear operators for discretizations of differential equations and scientific machine learning (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 framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

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)

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

Tutorials on math epidemiology and epidemiology informed deep learning methods

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

A Julia package for training recurrent neural networks (RNNs), vanilla neural ordinary differential equations (nODEs) and gated neural ordinary differential equations (gnODEs).