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Preface to the theme issue 'physics-informed machine learning and its structural integrity applications'.

Shun-Peng Zhu1, Abílio M P De Jesus2, Filippo Berto3

  • 1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.

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Summary

Physics-informed machine learning enhances structural integrity assessments by integrating physical laws into models. This approach improves generalization and reliability for engineering systems, advancing material science and safety evaluations.

Keywords:
failure mechanism modellingmachine learningphysics-informed machine learningprognostic and health managementstructural integrity

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Area of Science:

  • Engineering
  • Data Science
  • Physics

Background:

  • Machine learning (ML) offers significant potential in engineering but lacks physical meaning and generalizability.
  • Purely data-driven models struggle with novel scenarios and physical interpretability.
  • Integrating physics into ML is crucial for robust engineering applications.

Purpose of the Study:

  • To provide an updated review of physics-informed machine learning (PIML).
  • To highlight PIML applications in structural integrity and safety assessment.
  • To explore advanced ML algorithms for real-time data analysis in material science.

Main Methods:

  • Incorporating physical principles into machine learning algorithms.
  • Developing sophisticated ML techniques for data analysis.
  • Focusing on material science, fatigue, and fracture mechanics.

Main Results:

  • PIML models demonstrate improved generalization and physical interpretability.
  • Enhanced accuracy and productivity in real-time data processing.
  • Potential for designing new materials and structures with reliable safety assessments.

Conclusions:

  • Physics-informed machine learning is a transformative field for engineering.
  • PIML enhances the reliability and applicability of ML in structural integrity.
  • This research paves the way for future advancements in material design and safety.