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.
arXiv preprint arXiv:2105.10172 (2021)
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Field study of a knowledge-driven TinyMLOps pipeline on 4.4 GB of offshore wind data yields 0.84 AUC load-peak prediction in 32 kB on Cortex-M4 with 11% reported reduction in non-productive time.
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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.
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Tiny Machine-Learning Operations within Cyber-Physical Systems: a Field Study
Field study of a knowledge-driven TinyMLOps pipeline on 4.4 GB of offshore wind data yields 0.84 AUC load-peak prediction in 32 kB on Cortex-M4 with 11% reported reduction in non-productive time.