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SciMLBenchmarksOutput
PublicSciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
- A standard library of components to model the world and beyond
- The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
- Reservoir computing utilities for scientific machine learning (SciML)
- The Base interface of the SciML ecosystem
PoissonRandom.jl
PublicFast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)sciml.ai
PublicThe SciML Scientific Machine Learning Software Organization WebsiteGlobalSensitivity.jl
PublicRobust, Fast, and Parallel Global Sensitivity Analysis (GSA) in JuliaLinearSolve.jl
PublicLinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.DiffEqNoiseProcess.jl
PublicA library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)- SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia
- Boundary value problem (BVP) solvers for scientific machine learning (SciML)
Catalyst.jl
PublicChemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.- 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)
ModelingToolkit.jl
PublicAn acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
- CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.
PreallocationTools.jl
PublicTools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes- Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.
BaseModelica.jl
PublicSciMLStructures.jl
Public- A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
MuladdMacro.jl
PublicThis package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem- Symbolic-Numeric Universal Differential Equations for Automating Scientific Machine Learning (SciML)
QuasiMonteCarlo.jl
PublicLightweight 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)