Physics-informed machine learning and its real-world applications
- Submission status
- Closed
- Submission deadline
Advances in machine learning (ML) and deep learning (DL) are undoubtedly enabling significant breakthroughs in all areas of science and technology. ML/DL models, however, do not necessarily obey the fundamental governing laws of physical systems and often fail to describe and predict scenarios beyond the ones they have been trained on. In addition, training deep neural networks requires a huge amount of quality data, which is not always available for scientific problems. To solve these challenges, a new paradigm that integrates physical principles into ML models is emerging: physics-informed machine learning. Incorporating physics into ML models makes it possible to build physically consistent predictive models which are faster to train, more generalizable, interpretable, and trustworthy.
This Collection aims to gather the latest advances in physics-informed machine learning applications in sciences and engineering. Submissions that provide evidence of scalable, robust, and reliable physics-informed machine learning approaches for large-scale, real-world applications are particularly welcome.
Editors
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Eleni Chatzi
Swiss Federal Institute of Technology (ETH) Zürich, Switzerland
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Marta D'Elia
Sandia National Laboratories, USA
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Jian-Xun Wang
University of Notre Dame, USA
Eleni Chatzi is an Associate Professor and Chair of Structural Mechanics and Monitoring at the Department of Civil, Environmental and Geomatic Engineering of ETH Zürich. Her research interests include the fields of Structural Health Monitoring (SHM), structural dynamics, and data-driven condition assessment of engineered systems. Her work in the domain of self-aware infrastructure was recognized with the 2020 Walter L. Huber Research prize, awarded by the American Society of Civil Engineers (ASCE). Dr Chatzi has been an Editorial Board Member for Scientific Reports since 2021.
Marta D'Elia is a Principal Member of the Technical Staff at Sandia National Laboratories, currently part of the Data Science and Computing group at the California site. She is a co-founder of the One Nonlocal World project. Her research interests include nonlocal modeling and simulation, optimization and optimal control, and scientific machine learning. Dr D’Elia has been an Editorial Board Member for Scientific Reports since 2022.
Jian-Xun Wang is an assistant professor of Aerospace and Mechanical Engineering at the University of Notre Dame. He is a recipient of 2021 NSF CAREER Award. His research focuses on scientific machine learning, data-driven computational modelling, Bayesian data assimilation, and uncertainty quantification. Dr Wang has been an Editorial Board Member for Scientific Reports since 2022.
