Code Listings models of The Years of the Switch and the Dream of The Singularity 2020 CE
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Updated
Nov 26, 2021 - Julia
Code Listings models of The Years of the Switch and the Dream of The Singularity 2020 CE
Parameter Estimation of ODE/DDE Models in Julia
Inoue, K. et al. Oscillation dynamics underlie functional switching of NF-κB for B-cell activation. npj Syst. Biol. Appl. 2, 16024 (2016).
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
Solvers for steady states in scientific machine learning (SciML)
Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.
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
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
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.
The Base interface of the SciML ecosystem
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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
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.
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
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