News
29 September 2023

Philosophical Transactions of the Royal Society A: Mathematical, Physical And Engineering Sciences new OA issue

This collection discusses several critical issues related to learning from massive amounts of data, and highlights current research endeavours and the challenges to data science in structural integrity and safety, especially incorporating physics into machine learning models.

© Royal Society Publishing
This theme issue explores the advances in physics­informed machine learning (PIML) and its structural integrity applications through accurate failure mechanism modelling, combining either deterministic or probabilistic analyses by using Artificial intelligence (Al) methods. PIML improves consistency with prior knowledge, extrapolation performance, prediction accuracy, interpretability and computational efficiency and reduces dependence on training data, which provides an excellent opportunity to discover new physics under small samples and ambiguous physical mechanisms. 
Access the content online. 

Related topics

Materials & manufacturing Digital
Return to listing