<ul><li>Agent-based modeling (ABM) is computationally intensive and not analytically tractable for understanding self-organizing biological systems.</li><li>Equation learning (EQL) methods can derive continuum models from ABM data, but concerns about generalizability arise due to the need for extensive simulations for each parameter set.</li><li>Multi-experiment equation learning (ME-EQL) introduces two methods - one-at-a-time ME-EQL (OAT ME-EQL) and embedded structure ME-EQL (ES ME-EQL) to enhance generalizability across parameter space.</li><li>Demonstrated using birth-death mean-field and on-lattice agent-based models, ME-EQL methods reduce relative error in recovering parameters from agent-based simulations, with OAT ME-EQL showing better generalizability.</li></ul>

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

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