Modeling in the Era of Generative AI
Special Issue Editors
Interests: mathematical modeling; fuzzy logic; optimization
Interests: information systems and technologies; business intelligence; big data; intelligent software agents; machine learning; data mining; multi-criteria decision making; fuzzy sets
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Special Issue Information
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 Special Issue aims to explore the intersection between generative AI and modeling, highlighting new methodologies, frameworks, tools, and applications that reflect this paradigm shift. 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 in various fields.
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:
- GAI-augmented forecasting and simulation methods;
- The integration of LLMs in data-driven and knowledge-based modeling;
- Model explainability and interpretability using GAI tools;
- The use of GAI for data preparation, feature engineering, and augmentation;
- Domain-specific modeling in finance, education, healthcare, industry, and governance;
- Ethical and practical challenges in AI-assisted model design and validation;
- Hybrid modeling frameworks combining classical and generative approaches.
We welcome original research papers and review articles. By fostering interdisciplinary dialogue, this Special Issue seeks to improve our understanding of how generative AI is reshaping modeling paradigms and identify the opportunities and challenges that lie ahead.
Dr. Tania Yankova
Prof. Dr. Galina Ilieva
Guest Editors
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Keywords
- generative artificial intelligence
- modeling
- statistical forecasting
- simulation
- optimization
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