sebapersson/PEtab.jl

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

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

python Parameter EStimation TOolbox

A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications

High level functions for analyzing the output of simulations

PEtab - an SBML and TSV based data format for parameter estimation problems in systems biology

Derivative-free parameter calibration and uncertainty quantification for expensive models using ensemble Kalman methods

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

High-Performance Scientific Modeling with Julia and SciML