Journal
IEEE ACCESS
Volume 8, Issue -, Pages 71050-71073Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2987324
Keywords
Mathematical model; Physics; Computational modeling; Machine learning; Data models; Cyber-physical systems; Sensors; Cyber-physical systems; deep learning; deep neural networks; hybrid models; model-based; machine learning; physics guided; physics informed; physics prior; theory guided
Categories
Funding
- Defense Advanced Research Projects Agency (DARPA) [HR0011-18-9-0037]
- Naval Surface Warfare Center (NSWC) NEEC [N00174-19-0025]
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A multitude of cyber-physical system (CPS) applications, including design, control, diagnosis, prognostics, and a host of other problems, are predicated on the assumption of model availability. There are mainly two approaches to modeling: Physics/Equation based modeling (Model-Based, MB) and Machine Learning (ML). Recently, there is a growing consensus that ML methodologies relying on data need to be coupled with prior scientific knowledge (or physics, MB) for modeling CPS. We refer to the paradigm that combines MB approaches with ML as hybrid learning methods. Hybrid modeling (HB) methods is a growing field within both the ML and scientific communities, and are recognized as an important emerging but nascent area of research. Recently, several works have attempted to merge MB and ML models for the complete exploitation of their combined potential. However, the research literature is scattered and unorganized. So, we make a meticulous and systematic attempt at organizing and standardizing the methods of combining ML and MB models. In addition to that, we outline five metrics for the comprehensive evaluation of hybrid models. Finally, we conclude by shedding some light on the challenges of hybrid models, which we, as a research community, should focus on for harnessing the full potential of hybrid models. An additional feature of this survey is that the hybrid modeling work has been discussed with a focus on modeling cyber-physical systems.
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