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Creators/Authors contains: "Griesemer, Sam"

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  1. ; ; ; ; (, Machine Learning for Computational Science and Engineering)
    Abstract Physics-informed machine learning (PIML), the combination of prior physics knowledge with data-driven machine learning models, has emerged as an effective means of mitigating a shortage of training data, increasing model generalizability, and ensuring physical plausibility of results. In this paper, we survey a wide variety of recent works in PIML and summarize them from three key aspects: 1) motivations of PIML, 2) physics knowledge in PIML, and 3) methods of physics knowledge integration in PIML. We additionally discuss current challenges and corresponding research opportunities in PIML. 
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    Free, publicly-accessible full text available June 1, 2026
  2. ; ; (, Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 10, 2025
  3. ; ; ; ; (, Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 10, 2025