SciML/DiffEqGPU.jl

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

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

An algebraic modeling system for GPUs

Package for writing high-level code for parallel high-performance stencil computations that can be deployed on both GPUs and CPUs

GPU-capable dynamical core for the CliMA Earth System Model: spectral-element and finite-difference discretization tools

Cross-architecture parallel algorithms for Julia's CPU and GPU backends. Targets multithreaded CPUs, and GPUs via Intel oneAPI, AMD ROCm, Apple Metal, Nvidia CUDA.