Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges

Abstract

The computerized simulations of physical and socio-economic systems have proliferated in the past decade, at the same time, the capability to develop high-fidelity system predictive models is of growing importance for a multitude of reliability and system safety applications. Traditionally, methodologies for predictive modeling generally fall into two different categories, namely physics-based approaches and machine learning-based approaches. There is a growing consensus that the modeling of complex engineering systems requires novel hybrid methodologies that effectively integrate physics-based modeling with machine learning approaches, referred to as physics-informed machine learning (PIML). Developing advanced PIML techniques is recognized as an important emerging area of research, which could be particularly beneficial in addressing reliability and system safety challenges. With this motivation, this paper provides a review of the state-of-the-art of physics-informed machine learning methods in reliability and system safety applications. The paper highlights different efforts towards aggregating physical information and data-driven models as grouped according to their similarity and application area within each group. The goal is to provide a collection of research articles presenting recent developments of this emergent topic, and shed light on the challenges and future directions which we, as a research community, should focus on for harnessing the full potential of advanced PIML techniques for reliability and safety applications.