Philosophical Transactions of the Royal Society A: Physics-informed machine learning and its structural integrity applications

Free to access online, this theme issue explores advances in physics-informed machine learning (PIML) and its applications in structural integrity, highlighting current research and challenges in using artificial intelligence and data science to understand and predict structural performance and failure.

Published by Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, this second theme issue explores the advances in physics informed machine learning (PIML) and its structural integrity applications through accurate failure mechanism modelling, combining either deterministic or probabilistic analyses by using Artificial intelligence (AI) methods.

The 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. In addition to this, it consider how 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.

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