A Review of Physics-Informed Machine Learning in Fluid Mechanics

Pushan Sharma, Wai Tong Chung, Bassem Akoush and Matthias Ihme ()
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Pushan Sharma: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
Wai Tong Chung: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
Bassem Akoush: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
Matthias Ihme: Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA

Energies, 2023, vol. 16, issue 5, 1-21

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.

Keywords: physics-informed machine learning; PDE-preserved learning; deep neural network; fluid mechanics; Navier–Stokes (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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