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2 articles for “PIML”

• Published Subscription Review Article 8 Oct 2025

  Integrating AI and ML in Tribology: A Review of Current Trends and Future Prospects

  By Darshan Kulkarni, A. B. Kakade

  Abstract: This review paper explores the growing integration of artificial intelligence (AI) and machine learning (ML) within the field of tribology. Tribology, the study of friction, wear, and lubrication, is crucial for Improving the performance and longevity of mechanical systems. This review explores the role of AI and machine learning techniques, including artificial neural networks (ANNs), support vector machines (SVMs), and physics-informed machine learning (PIML)can be used to solve difficult tribological …

  Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 3, 2025 · pp. 56–60 Read article →

• Published Subscription Review Article 13 Oct 2025

  The Convergence of AI and Composites - A Review Anchored in Patent Trends

  By Silambarasan B., Gayathiri N.R., Thirumaraiselvi C., Mahendra Boopathi M., Hariprasad P., Gokulkumar S.

  Abstract: The integration of artificial intelligence (AI) and machine learning (ML) techniques is revolutionizing the design, analysis, and optimization of polymer (PC/FRP), metal (MC), and ceramic matrix composites (CC). Techniques such as artificial neural networks (ANN), deep learning (DL), genetic algorithms (GA), and physics-informed machine learning (PIML) are employed to enhance property estimation, process optimization, and predictive modeling. These AI-driven frameworks enable virtual testing, application-specific material design, and real-time decision-making, while …

  Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 182–198 Read article →