On the generality of tensor basis neural networks for turbulent scalar flux modeling

Milani, Pedro M.; Ling, Julia; Eaton, John K.

doi:10.1016/j.icheatmasstransfer.2021.105626

Cited by 14 publications

(1 citation statement)

References 29 publications

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“…In this a posteriori study, TBNN simulations accurately predicted the occurence of corner vortices, which vanilla ML models failed to predict. Since then, the TBNN has been extended toward problems in turbulent heat transfer [83] as well as improved [84] to consider boundary conditions, non-locality, and Reynolds number embeddings. The TBNN has inspired other architectures such as the Vector Basis Neural Network (VBNN) [85], which has been modified to specifically predict the divergence of the subgrid-scale stresses.…”

Section: Physics-informed Architecturementioning

confidence: 99%

A Review of Physics-Informed Machine Learning in Fluid Mechanics

et al. 2023

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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.

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Section: Physics-informed Architecturementioning

confidence: 99%