Hybrid Modeling Integrating Artificial Intelligence and Modeling & Simulation Paradigms

This paper discusses the complementary relationship between Modeling and Simulation (M&S) and Artificial Intelligence (AI) methods like machine learning. While M&S uses algorithms to model system behavior from input parameters, AI learns patterns from correlation in data. The paper argues that hybrid models combining M&S and AI can be more powerful than either alone. It provides a conceptual framework showing how M&S and AI can be integrated in sequential, parallel, complementary or competitive configurations. Several example applications are given where AI enhances M&S and vice versa, such as using AI to optimize simulation parameters, generate synthetic training data for AI from simulations, interpret AI model behavior through simulation, and automate aspects of simulation development with AI assistance. The potential benefits of hybrid AI/M&S modeling span improved accuracy, efficiency, trustworthiness and cross-disciplinary collaboration. The paper calls for further research developing a solid theoretical foundation for merging these complementary paradigms.