Nonlinear Mixed Effects Modeling of Deterministic and Stochastic Dynamical Systems in Wolfram Mathematica
Paper i proceeding, 2021

Nonlinear mixed effects (NLME) modeling is a powerful tool to analyze timeseries data from several individual entities in an experiment. In this paper, we give a brief overview of a package for NLME modeling in Wolfram Mathematica entitled NLMEModeling, implementing the first-order conditional estimation method with sensitivity equation-based gradients for parameter estimation. NLMEModeling supports mixed effects modeling of dynamical systems where the underlying dynamics are described by either ordinary or stochastic differential equations combined with observation equations with flexible observation error models. Moreover, NLMEModeling is a user-friendly package with functionality for parameter estimation, model diagnostics (such as goodness-of-fit analysis and visual predictive checks), and model simulation. The package is freely available and provides an extensible add-on to Wolfram Mathematica.

Stochastic differential equations

Ordinary differential equations

Nonlinear mixed effects

Modeling software

Wolfram Mathematica

First-order conditional estimation (FOCE)

Dynamical system models

Författare

Jacob Leander

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

AstraZeneca AB

Joachim Almquist

AstraZeneca R&D

Anna Johnning

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Julia Larsson

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Mats Jirstrand

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

IFAC-PapersOnLine

2405-8963 (ISSN)

Vol. 54 7 409-414

19th IFAC Symposium on System Identification (SYSID)
Padova, Italy,

Ämneskategorier

Teknisk mekanik

Sannolikhetsteori och statistik

Reglerteknik

DOI

10.1016/j.ifacol.2021.08.394

Mer information

Senast uppdaterat

2022-03-02