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

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.