[2506.08916] Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL)

Computer Science > Machine Learning arXiv:2506.08916 (cs) [Submitted on 10 Jun 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL) Authors:Maria-Veronica Ciocanel, John T. Nardini, Kevin B. Flores, Erica M. Rutter, Suzanne S. Sindi, Alexandria Volkening View a PDF of the paper titled Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL), by Maria-Veronica Ciocanel and 5 other authors View PDF HTML (experimental) Abstract:Agent-based modeling (ABM) is a powerful tool for understanding self-organizing biological systems, but it is computationally intensive and often not analytically tractable. Equation learning (EQL) methods can derive continuum models from ABM data, but they typically require extensive simulations for each parameter set, raising concerns about generalizability. In this work, we extend EQL to Multi-experiment equation learning (ME-EQL) by introducing two methods: one-at-a-time ME-EQL (OAT ME-EQL), which learns individual models for each parameter set and connects them via interpolation, and embedded structure ME-EQL (ES ME-EQL), which builds a unified model library across parameters. We demonstrate these methods using a birth--death mean-field model and an on-lattice agent-based model of birth, death, and migration with spatial structure. Our results show that both meth...