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Preface to the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

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

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Physics-informed machine learning offers new engineering solutions by overcoming data limitations. This approach enhances structural integrity analysis with high precision and efficiency.

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

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Area of Science:

  • Engineering
  • Machine Learning
  • Physics

Background:

  • Purely data-driven machine learning methods face challenges in engineering applications, including lack of interpretability and significant data requirements.
  • Emerging physics-informed machine learning (PIML) presents a promising alternative for intelligent engineering problem-solving.
  • This research is part of a special issue focused on PIML and its applications in structural integrity.

Purpose of the Study:

  • To explore the potential of physics-informed machine learning for engineering problems.
  • To demonstrate practical applications of PIML in structural integrity.
  • To highlight PIML's advantages over traditional data-driven methods.

Main Methods:

  • Integration of physical laws and principles into machine learning algorithms.
  • Development and application of knowledge-driven machine learning models.
  • Case studies focusing on structural integrity analysis using PIML.

Main Results:

  • PIML methods show significant potential for solving complex engineering problems with enhanced precision.
  • Demonstrated high efficiency in structural integrity applications through PIML.
  • Overcame limitations of pure data-driven approaches by incorporating physical knowledge.

Conclusions:

  • Physics-informed machine learning is a crucial research direction for the future of intelligent engineering.
  • PIML offers a powerful framework for precise and efficient solutions in structural integrity.
  • Knowledge-driven machine learning is poised to profoundly impact future engineering research.