倉庫
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.
Various developer materials, like PDFs, notes, derivations, etc. for differential equations and scientific machine learning (SciML)
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
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
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)
Finds relationships between the parameters of a mathematical model
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
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Mathematical 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.
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices
SBML differential equation and chemical reaction model (Gillespie simulations) for Julia's SciML ModelingToolkit
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
Global documentation for the Julia SciML Scientific Machine Learning Organization