Simulation and artificial intelligence are becoming a single, powerful ecosystem for understanding and shaping the world. From digital twins and reinforcement learning to large language models and synthetic data, this volume captures how AI and modeling and simulation together are redefining how we explore complexity, uncertainty, and decision-making. Artificial Intelligence and Modeling and Simulation brings together leading researchers who show how AI can support every stage of a simulation study, from model specification and input modeling to execution, verification, and analysis. It also demonstrates how simulations provide critical data, training environments, and validation platforms for AI. Chapters are supplemented by exercises, including in-depth exploratory questions that provide a guided, hands-on experience. The volume offers a coherent roadmap for navigating an increasingly interconnected ecosystem of models, data, and learning algorithms. Topics and features:·         Complete coverage of the AI–simulation pipeline, from conceptual modeling and input modeling to verification, validation, and result interpretation·         State-of-the-art methods including surrogate modeling, reinforcement learning, and large language models applied directly to modeling and simulation problems·         Rigorous treatment of verification, validation, and benchmarking, including risks, uncertainty, and the limits of black-box models·         Interdisciplinary case studies spanning healthcare, energy, political history, wildlife education, and evacuationThis book provides comprehensive research guidance on methods, applications, and open problems at the interface of artificial intelligence and modeling and simulation. This is written for researchers and graduate students who seek research methods in AI and simulation, as well as for industry professionals and practitioners in data science or digital twins. The book is edited by Dr. Philippe Giabbanelli (full professor by research at Old Dominion University, USA) and Dr. Istvan David (assistant professor at McMaster University, Canada). Contributions to the chapters come from 28 authors across 20 institutions (reflecting perspectives from academia, industry, and national laboratories) in four countries.

Simulation and artificial intelligence are becoming a single, powerful ecosystem for understanding and shaping the world. From digital twins and reinforcement learning to large language models and synthetic data, this volume captures how AI and modeling and simulation together are redefining how we explore complexity, uncertainty, and decision-making. Artificial Intelligence and Modeling and Simulation brings together leading researchers who show how AI can support every stage of a simulation study, from model specification and input modeling to execution, verification, and analysis. It also demonstrates how simulations provide critical data, training environments, and validation platforms for AI. Chapters are supplemented by exercises, including in-depth exploratory questions that provide a guided, hands-on experience. The volume offers a coherent roadmap for navigating an increasingly interconnected ecosystem of models, data, and learning algorithms. Topics and features:·         Complete coverage of the AI–simulation pipeline, from conceptual modeling and input modeling to verification, validation, and result interpretation·         State-of-the-art methods including surrogate modeling, reinforcement learning, and large language models applied directly to modeling and simulation problems·         Rigorous treatment of verification, validation, and benchmarking, including risks, uncertainty, and the limits of black-box models·         Interdisciplinary case studies spanning healthcare, energy, political history, wildlife education, and evacuationThis book provides comprehensive research guidance on methods, applications, and open problems at the interface of artificial intelligence and modeling and simulation. This is written for researchers and graduate students who seek research methods in AI and simulation, as well as for industry professionals and practitioners in data science or digital twins. The book is edited by Dr. Philippe Giabbanelli (full professor by research at Old Dominion University, USA) and Dr. Istvan David (assistant professor at McMaster University, Canada). Contributions to the chapters come from 28 authors across 20 institutions (reflecting perspectives from academia, industry, and national laboratories) in four countries.