Integrating Physics-Based Modeling with Machine Learning: A Survey

By Jared D. Willard, Xiaowei Jia, Shaoming Xu, Michael S. Steinbach, Vipin Kumar. arXiv.org, 2020

416 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6965bc6d26fc910a6387cd6d423b35fd9e1d358b

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Citations in the corpus

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “For further information, readers are recommended to read the recent comprehensive survey about integrating physics-based modeling with ML as presented byWillard et al.(341) In the study by Zhang et al.”
Characterizing possible failure modes in physics-informed neural networks (2021, background): “In this vein, there have been a number of ML approaches to incorporate scientific knowledge into such problems while keeping the automatic, data-driven estimates of the solution [2, 17, 33, 39].”
Digital twin, physics-based model, and machine learning applied to damage detection in structures (2020, background): “Recently, lots of researches are dedicated to leverage computational models, by integrating physics-based models with machine learning [9, and references therein].”
Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics (2021, methodology): “Therefore, the N-CMAPSS dataset can also be used to develop new physics-informed machine learning algorithms [28].”
On closures for reduced order models—A spectrum of first-principle to machine-learned avenues (2021, unknown): “–131 Projection-based ROMs have been explored for decades, and these explorations have been paying off in many applications.”
A survey of uncertainty in deep neural networks (2021, background): “Such physic guided models offer a variety of possibilities to include explicit knowledge as well as practical uncertainty representations into a deep learning framework [344], [345].”
Fusing Physics-based and Deep Learning Models for Prognostics (2020, background): “Depending on what type of information is processed and how the pieces of information are combined, different types of hybrid architectures have been suggested [15,16].”
Physics-informed machine learning: case studies for weather and climate modelling (2021, background): “[12] broadly survey research work in this field that applies across the sciences.”
Aligning artificial intelligence with climate change mitigation (2022, unknown): “Increasing effect on GHG emissions”