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- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
diffeqpy
PublicSolving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organizationSciMLBase.jl
PublicThe Base interface of the SciML ecosystemSciMLDocs
PublicGlobal documentation for the Julia SciML Scientific Machine Learning Organization- An 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
DiffEqBase.jl
PublicThe lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems- Symbolic-Numeric Universal Differential Equations for Automating Scientific Machine Learning (SciML)
- A common solve function for scientific machine learning (SciML) and beyond
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.- SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Developer documentation for the SciML scientific machine learning ecosystem's differential equation solvers
OptimizationBase.jl
Public- A standard library of components to model the world and beyond
FiniteVolumeMethod.jl
PublicSolver for two-dimensional conservation equations using the finite volume method in Julia.- High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
ModelOrderReduction.jl
PublicHigh-level model-order reduction to automate the acceleration of large-scale simulations- Fast and automatic structural identifiability software for ODE systems
DiffEqDocs.jl
PublicDocumentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystemSciMLSensitivity.jl
PublicA component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.DiffEqCallbacks.jl
PublicA library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solversNeuralOperators.jl
PublicSurrogates.jl
PublicSurrogate modeling and optimization for scientific machine learning (SciML)Optimization.jl
PublicMathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
- 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)
- Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices