SciML/SciMLBenchmarks.jl

A Library for Advanced Neural PDE Solvers.

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.

A library for scientific machine learning and physics-informed learning

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

Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

A framework for reproducible, comparable benchmarks

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation