Modeling in the Era of Generative AI, 2nd Edition

Dear Colleagues,

In the era of rapidly evolving generative artificial intelligence (GAI), modeling practices across scientific, engineering, and business domains are undergoing a fundamental transformation. Traditional modeling techniques, ranging from statistical forecasting to simulation and optimization, are increasingly being integrated with, or influenced by, generative AI technologies. This paradigm shift is redefining the very nature of how information is represented, processed, and operationalized in complex systems.

This Special Issue aims to explore the intersection between generative AI and modeling, highlighting the new methodologies, frameworks, tools, and applications that reflect this transformation. Whether through automating model development, enhancing predictive accuracy, generating synthetic data, or supporting complex decision-making processes, GAI is becoming a pivotal force in how we conceptualize and operationalize models across diverse fields. This Special Issue seeks to advance both the theoretical foundations and practical implementations that define this emerging landscape.

We seek contributions from researchers and practitioners that focus on advancing modeling strategies in the GAI era. Submissions may cover, but are not limited to, the following topics:

• Novel theoretical frameworks and information-theoretic analyses of generative model behavior and emergent capabilities;
• Integration of large language models (LLMs) in data-driven and knowledge-based modeling;
• GAI-augmented forecasting, simulation, and optimization methods;
• Automated model development, meta-learning, and human–AI collaborative workflows;
• Model explainability, interpretability, and fairness using GAI tools;
• Data preparation, feature engineering, and synthetic data generation for privacy-preserving and rare-event modeling;
• Physics-informed generative models, causal reasoning, and neuro-symbolic approaches;
• Domain-specific applications in smart cities, digital twins, healthcare, finance, education, and governance;
• Scalable architectures, edge AI, and green AI for efficient GAI deployment;
• Ethical challenges, algorithmic bias, trustworthiness, and regulatory considerations in AI-assisted modeling;
• Multimodal generation, cross-modal information synthesis, and real-time adaptive modeling.

We welcome the submission of original research papers, comprehensive review articles, and case studies that demonstrate real-world impact. By fostering interdisciplinary dialog, this Special Issue seeks to deepen our understanding of how generative AI is reshaping modeling paradigms, as well as to identify the opportunities and challenges that lie ahead. We invite contributions from academia, industry, and government institutions, aiming to collectively chart the future of modeling in the age of generative AI.

Dr. Tania Yankova
Guest Editor

Manuscript Submission Information

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