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

Local, global, and beyond optimization for scientific machine learning (SciML)

Operator learning in Julia.

A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automa…

Benchmarks for scientific machine learning (SciML) software and differential equation solvers

Stack trace visualizer

Write flame graphs to SVG format and explore them interactively in Jupyter, Pluto, etc.

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

Relax! Flux is the ML library that doesn't make you tensor

MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.

A fast and modern CAS for a fast and modern language.

The Base interface of the SciML ecosystem

Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)

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

Notebook for running Julia on Google Colab

Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF)

DiffEq solvers for stochastic differential equations

Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations

Deep BSDE solver in TensorFlow