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

ArXi:2506.08916v2 Announce Type: replace 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