Physics-informed neural networks (PINNs) for fluid mechanics: a review

Cai, Shengze; Mao, Zhiping; Wang, Zhicheng; Yin, Minglang; Karniadakis, George Em

doi:10.1007/s10409-021-01148-1

Title: Physics-informed neural networks (PINNs) for fluid mechanics: a review

Journal Article · Sun Jan 23 00:00:00 EST 2022 · Acta Mechanica Sinica

DOI:https://doi.org/10.1007/s10409-021-01148-1· OSTI ID:2282982

Cai, Shengze ^[1]; Mao, Zhiping ^[2]; Wang, Zhicheng ^[3]; Yin, Minglang ^[1]; Karniadakis, George Em ^[1]

Brown University, Providence, RI (United States)
Xiamen University (China)
Dalian University of Technology (China)

Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is complex, and we cannot tackle high-dimensional problems governed by parametrized NSE. Moreover, solving inverse flow problems is often prohibitively expensive and requires complex and expensive formulations and new computer codes. Here, we review flow physics-informed learning, integrating seamlessly data and mathematical models, and implement them using physics-informed neural networks (PINNs). Here, we demonstrate the effectiveness of PINNs for inverse problems related to three-dimensional wake flows, supersonic flows, and biomedical flows.

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Research Organization:: Brown Univ., Providence, RI (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0019453

OSTI ID:: 2282982

Journal Information:: Acta Mechanica Sinica, Vol. 37, Issue 12; ISSN 0567-7718

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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42 ENGINEERING
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Inverse problems
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Title: Physics-informed neural networks (PINNs) for fluid mechanics: a review

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