Perspectives on the integration between first-principles and data-driven modeling

Bradley, William; Kim, Jinhyeun; Kilwein, Zachary; Blakely, Logan; Eydenberg, Michael; Jalvin, Jordan; Laird, Carl; Boukouvala, Fani

doi:10.1016/j.compchemeng.2022.107898

Title: Perspectives on the integration between first-principles and data-driven modeling

Journal Article · Wed Jun 22 00:00:00 EDT 2022 · Computers and Chemical Engineering

DOI:https://doi.org/10.1016/j.compchemeng.2022.107898· OSTI ID:1889102

^[1]; Kim, Jinhyeun ^[1]; Kilwein, Zachary ^[1]; Blakely, Logan ^[2]; Eydenberg, Michael ^[2]; Jalvin, Jordan ^[2]; Laird, Carl ^[2]; Boukouvala, Fani ^[1]

Georgia Institute of Technology, Atlanta, GA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. Further 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 hybridization 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.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); National Network for Manufacturing Innovation (NNMI); National Science Foundation (NSF); Georgia Tech

Grant/Contract Number:: NA0003525 GR10002225; 1336386; 1944678

OSTI ID:: 1889102

Report Number(s):: SAND2022-12844J; 710091

Journal Information:: Computers and Chemical Engineering, Vol. 166; ISSN 0098-1354

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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