Computes the boundary crossing probability for a general diffusion process and time-dependent boundary.

Code Listings models of The Years of the Switch and the Dream of The Singularity 2020 CE

Workshop materials for training in scientific computing and scientific machine learning

A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)

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

Differential equation problem specifications and scientific machine learning for common financial models

Solvers for steady states in scientific machine learning (SciML)

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

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

A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)

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

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

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

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

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.

Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.

Linear operators for discretizations of differential equations and scientific machine learning (SciML)