#ordinary-differential-equations (12 Repositories)
Ranked open-source repositories tagged with #ordinary-differential-equations, scored by pull request acceptance likelihood and maintainer engagement velocity.
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12 repositories tagged #ordinary-differential-equations
SciML/ModelingToolkit.jl
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
SciML/OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
SciML/SciMLBase.jl
The Base interface of the SciML ecosystem
SciML/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
control-toolbox/OptimalControl.jl
Model and solve optimal control problems in Julia, both on CPU and GPU.
SciML/DiffEqFlux.jl
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
SciML/Sundials.jl
Julia interface to Sundials, including a nonlinear solver (KINSOL), ODEs (CVODE and ARKODE), and DAEs (IDA)
mpmath/mpmath
Python library for arbitrary-precision floating-point arithmetic
SciML/ModelingToolkitStandardLibrary.jl
A standard library of components to model the world and beyond
SciML/DiffEqBayes.jl
Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
SciML/diffeqpy
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
SciML/DiffEqBase.jl
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems