A multi-stage pipeline uses model-based screening followed by ML surrogates to explore high-dimensional stochastic agent-based models and identify unstable regions.
arXiv preprint arXiv:2510.16742;2025 (2025)
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This survey synthesizes XAI methods with surrogate modeling workflows for simulations and outlines a research agenda to embed explainability into simulation-driven design and decision-making.
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From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
A multi-stage pipeline uses model-based screening followed by ML surrogates to explore high-dimensional stochastic agent-based models and identify unstable regions.
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Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making
This survey synthesizes XAI methods with surrogate modeling workflows for simulations and outlines a research agenda to embed explainability into simulation-driven design and decision-making.