Using a library of chemical reactions to fit systems of ordinary differential equations to agent-based models: a machine learning approach

2023/08/31 by Pamela Burrage, Burrage, Pamela M., Hasitha N. Weerasinghe +3 · 1 citation
Mathematics · #34C60 #Dynamical Systems (math.DS) #FOS: Mathematics #Mathematical Biology Tumor Growth

paper · pdf · doi:10.48550/arxiv.2308.16431

openalex publication_date 2023/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper we introduce a new method based on a library of chemical reactions for constructing a system of ordinary differential equations from stochastic simulations arising from an agent-based model. The advantage of this approach is that this library respects any coupling between systems components, whereas the SINDy algorithm (introduced by Brunton, Proctor and Kutz) treats the individual components as decoupled from one another. Another advantage of our approach is that we can use a non-negative least squares algorithm to find the non-negative rate constants in a very robust, stable and simple manner. We illustrate our ideas on an agent-based model of tumour growth on a 2D lattice.

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