LAPSE:2023.20411
Published Article
LAPSE:2023.20411
A Review of Physics-Informed Machine Learning in Fluid Mechanics
March 17, 2023
Abstract
Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.
Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.
Record ID
Keywords
deep neural network, fluid mechanics, Navier–Stokes, PDE-preserved learning, physics-informed machine learning
Suggested Citation
Sharma P, Chung WT, Akoush B, Ihme M. A Review of Physics-Informed Machine Learning in Fluid Mechanics. (2023). LAPSE:2023.20411
Author Affiliations
Sharma P: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA [ORCID]
Chung WT: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA [ORCID]
Akoush B: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA [ORCID]
Ihme M: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Photon Science, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA [ORCID]
Chung WT: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA [ORCID]
Akoush B: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA [ORCID]
Ihme M: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Photon Science, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA [ORCID]
Journal Name
Energies
Volume
16
Issue
5
First Page
2343
Year
2023
Publication Date
2023-02-28
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en16052343, Publication Type: Review
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Published Article
LAPSE:2023.20411
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https://doi.org/10.3390/en16052343
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Mar 17, 2023
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