Fit Time-Series Data to Neural Differential Equations in Julia

A Julia package for the computation of hard, theoretically guaranteed bounds on the moments of jump-diffusion processes with polynomial data

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

R and Julia codes for case study 2 (Breast Cancer toxicity model) in the manuscript titled "Adding noise to Markov cohort state-transition models."

Efficiently generate Gaussian stochastic processes with heavy-tailed algebraic correlations.

Repository for stochastic methods scripts and functions.

Supplementary material for the preprint "Identifiability analysis for stochastic differential equation models in systems biology" available on bioRxiv.

A Julia package for critical transitions in dynamical systems with time-dependent forcing

Geometric Numerical Integration in Julia

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

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 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

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

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

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

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

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.