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October 11, 2025Journal of Computing and Information Science in EngineeringOpen Access

Physics-Informed Machine Learning in Design and Manufacturing: Status and Challenges

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Authors

LPLongye PanGLGuangfa LiTZTong Zhu

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Overview

Systematic review highlights physics-informed machine learning techniques in design and manufacturing, emphasizing challenges and methodologies.

Key Points

  • Physics-informed machine learning enables more interpretable and reliable models in design and manufacturing.
  • The approach categorizes techniques into hybrid models, physics-loss models, and physics-embedded architectures.
  • Challenges related to data quality and training methods are examined for enhancing machine learning's reliability.
  • Addressing ongoing challenges and opportunities could significantly advance the field of machine learning in practical applications.

Cite This Study

Pan et al. (2025) studied this question.

synapsesocial.com/papers/68e9b1b5ba7d64b6fc131fefhttps://doi.org/10.1115/1.4070100
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