Scientific-machine-learning Libraries

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Scientific-machine-learning Libraries

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.

This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations…

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Note that when running the tutorials, the packages are not automatically added. Thus you will need to add the packages manually or use the internal Project/Manifest tomls to instantiate the correct p…

Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

directly in Python. To install diffeqpy, use pip: and you're good! We can plot the solution values using matplotlib: We can utilize the interpolation to get a finer solution: or we can draw the phase…

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

SciMLBenchmarks.jl holds webpages, pdfs, and notebooks showing the benchmarks for the SciML Scientific Machine Learning Software ecosystem, including: The SciML Bench suite is made to be a comprehens…

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