Dde Collection

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“Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.”

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“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”

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💧Water

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“Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.”

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“Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization”

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“ A curated list of awesome Windows frameworks, libraries, software and resources for Red Teams”

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“File manager for deepin desktop environment.”

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“A 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.”

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“The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems”

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🔥Fire

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“A collection of resources for pulling real-time streaming data off of TDAmeritrade's ThinkOrSwim(TOS) platform; providing C, C++, Java and Python interfaces.”

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“Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem”

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“GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem”

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“The Base interface of the SciML ecosystem”

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“Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem”

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“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”

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“A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools”

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“A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers”

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“Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.”

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“A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations”