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Authors = Bassem Akoush ORCID = 0000-0001-5160-4451

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21 pages, 27086 KiB  
Review
A Review of Physics-Informed Machine Learning in Fluid Mechanics
by Pushan Sharma, Wai Tong Chung, Bassem Akoush and Matthias Ihme
Energies 2023, 16(5), 2343; https://doi.org/10.3390/en16052343 - 28 Feb 2023
Cited by 112 | Viewed by 25198
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 [...] Read more.
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. Full article
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