GPdoemd: a Python package for design of experiments for model discrimination

TL;DR

GPdoemd is an open-source Python package that enhances model discrimination by using Gaussian process surrogates and a new Jensen-Rényi divergence criterion to optimize experimental design, especially for black-box models.

Contribution

The paper introduces a novel design criterion based on Jensen-Rényi divergence and a Gaussian process surrogate approach for model discrimination, including an open-source implementation.

Findings

Effective discrimination for classical and new test cases.

Successful surrogate modeling of black-box models.

Improved experimental design efficiency.

Abstract

Model discrimination identifies a mathematical model that usefully explains and predicts a given system's behaviour. Researchers will often have several models, i.e. hypotheses, about an underlying system mechanism, but insufficient experimental data to discriminate between the models, i.e. discard inaccurate models. Given rival mathematical models and an initial experimental data set, optimal design of experiments suggests maximally informative experimental observations that maximise a design criterion weighted by prediction uncertainty. The model uncertainty requires gradients, which may not be readily available for black-box models. This paper (i) proposes a new design criterion using the Jensen-R\'enyi divergence, and (ii) develops a novel method replacing black-box models with Gaussian process surrogates. Using the surrogates, we marginalise out the model parameters with approximate inference. Results show these contributions working well for both classical and new test instances. We also (iii) introduce and discuss GPdoemd, the open-source implementation of the Gaussian process surrogate method.