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
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Computes the boundary crossing probability for a general diffusion process and time-dependent boundary.
A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)
Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
Differential equation problem specifications and scientific machine learning for common financial models
Workshop materials for training in scientific computing and scientific machine learning
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.
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
Solvers for steady states in scientific machine learning (SciML)
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
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
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)
A standard library of components to model the world and beyond
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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