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Physics-informed machine learning and its structural integrity applications: state of the art
Shun-Peng Zhu1, Lanyi Wang1, Changqi Luo1
1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
Summary
Physics-informed machine learning (PIML) enhances structural integrity assessments by integrating physical laws into models. This approach improves prediction accuracy and reduces data dependency, overcoming limitations of pure data-driven methods.
Area of Science:
- Engineering
- Computer Science
- Materials Science
Background:
- Machine learning (ML) shows promise for structural integrity but faces challenges like ignoring physical laws and poor extrapolation.
- Pure data-driven ML methods struggle with data requirements and generalization in critical component analysis.
Purpose of the Study:
- To review the integration of physical information into ML models, creating physics-informed machine learning (PIML).
- To explore PIML applications in structural integrity, including failure mechanism modeling and prognostic and health management (PHM).
Main Methods:
- Discusses various techniques for embedding physical information into ML algorithms.
- Reviews existing literature on PIML applications within the field of structural integrity.
Main Results:
- PIML demonstrates potential for enhanced consistency with prior knowledge and improved extrapolation performance.
- PIML offers better prediction accuracy, interpretability, and computational efficiency while reducing reliance on extensive training data.
Conclusions:
- PIML presents a significant advancement over traditional ML for structural integrity applications.
- Further research is needed to develop advanced PIML for robust engineering system integrity assurance.
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