Speakers
Description
Population models have long been grounded in natural intelligence: the human-driven theoretical frameworks that include nonlinear dynamics, bifurcation theory, PDEs, structured population models, and stochastic processes. These classical approaches remain indispensable for explaining underlying mechanisms, generating deep insight, and ensuring interpretability. In parallel, artificial intelligence provides powerful computational strategies that enable rapid calibration, enhanced forecasting, and the identification of hidden structure in complex datasets.
This minisymposium will explore the synergy between these complementary approaches and highlight recent advances in population modeling arising from both pillars of scientific inquiry. By bringing together these perspectives, the session aims to showcase cutting-edge developments in traditional theory-based modeling and AI-empowered methodologies, illustrating how their integration drives new understanding of population dynamics in ecology, epidemiology, and evolutionary biology.
