ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Modeling the dynamics of complex systems: incorporating AI and HPC into dynamic modeling
Ignacio J Martinez-Moyano
Source abstract
Abstract In many areas of animal science, the questions are dominated by dynamic problems: outcomes evolve over time, adapt to interventions, and emerge from interacting biological, managerial, and institutional processes operating across temporal and spatial scales. These systems exhibit accumulation, feedback, nonlinearities, and time delays that can make intuitive reasoning unreliable, and that can cause well-intended policies to underperform or generate unanticipated consequences. Dynamic modeling and simulation provide a rigorous way to make such mechanisms explicit and to test whether observed patterns of behavior can arise endogenously from system structure. This paper positions system dynamics modeling as a foundational approach for feedback-driven explanation and policy design in animal systems. It also highlights agent-based modeling as a natural approach to capture heterogeneity, networks, and localized interactions in system behavior. The paper describes how artificial intelligence can strengthen dynamic modeling workflows by scaling evidence synthesis, supporting transparent and auditable structure development, and expanding feasible calibration and updating methods under uncertainty. Finally, the paper describes how high-performance computing enables ensemble-based inference and robust policy evaluation by making large-scale sensitivity analysis, uncertainty quantification, calibration, and scenario development and testing routine. Across these themes, the paper emphasizes methodological discipline: artificial intelligence and high-performance computing should augment—not replace—critical thinking, causal reasoning, rigorous structure development, thoughtful analysis, and transparent reporting. The combined use of dynamic modeling, artificial intelligence, and high-performance computing can help animal science build interpretable, testable, and updateable models that support learning and timely decision making in complex systems.
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