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Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
Blue Brain Python Electrical Modeling Pipeline
List of papers studying machine learning through the lens of category theory
A set of Processing animations, which have commented code
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learni…
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
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
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Relax! Flux is the ML library that doesn't make you tensor
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 a…
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…