Perspectives on the Integration between First-Principles and Data-Driven Modeling
William Bradley, Jinhyeun Kim, Zachary Kilwein, Logan Blakely, Michael Eydenberg, Jordan Jalvin, Carl Laird, Fani Boukouvala
November 7, 2021
Efficiently embedding and/or integrating mechanistic information within data-driven models is essentially the only approach to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive comparison of the different techniques with respect to their differences and similarities, as well as advantages and limitations and future perspectives. Finally, we apply and illustrate hybrid modeling, physics-informed ML and model calibration via a chemical reactor case study.
gaussian process regression, hybrid modeling, Machine Learning, model calibration, neural networks, physics-informed machine learning
Suggested Citation
Bradley W, Kim J, Kilwein Z, Blakely L, Eydenberg M, Jalvin J, Laird C, Boukouvala F. Perspectives on the Integration between First-Principles and Data-Driven Modeling. (2021). LAPSE:2021.0801
Author Affiliations
Bradley W:
Kim J:
Kilwein Z:
Blakely L:
Eydenberg M:
Jalvin J:
Laird C:
Boukouvala F: Georgia Institute of Technology
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