High-resolution (HR) information of fluid flows, although preferable, is usually less accessible due to limited computational or experimental resources. In many cases, fluid data are generally sparse, incomplete, and possibly noisy. How to enhance spatial resolution and decrease the noise level of flow data is essential and practically useful. Deep learning (DL) techniques have been demonstrated to be effective for super-resolution (SR) tasks, which, however, primarily rely on sufficient HR labels for training. In this work, we present a novel physics-informed DL-based SR solution using convolutional neural networks (CNNs), which is able to produce HR flow fields from low-resolution (LR) inputs in high-dimensional parameter space. By leveraging the conservation laws and boundary conditions of fluid flows, the CNN-SR model is trained without any HR labels. Moreover, the proposed CNN-SR solution unifies the forward SR and inverse data assimilation for the scenarios where the physics is partially known, e.g., unknown boundary conditions. A new network structure is designed to enable not only the parametric SR but also the parametric inference for the first time. Several flow SR problems relevant to cardiovascular applications have been studied to demonstrate the proposed method's effectiveness and merit. A series of different LR scenarios, including LR input with Gaussian noises, non-Gaussian magnetic resonance imaging noises, and downsampled measurements given either well-posed or ill-posed physics, are investigated to illustrate the SR, denoising, and inference capabilities of the proposed method.
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Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
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July 2021
Research Article|
July 07 2021
Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
Han Gao;
Han Gao
a)
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame
, Indiana 46556, USA
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Luning Sun;
Luning Sun
b)
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame
, Indiana 46556, USA
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Jian-Xun Wang
Jian-Xun Wang
c)
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame
, Indiana 46556, USA
c)Author to whom correspondence should be addressed: [email protected]. Tel.: +1 540 3156512
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a)
Electronic mail: [email protected]. Tel.: +1 314 2553782
b)
Electronic mail: [email protected]. Tel.: +1 574 2505382
c)Author to whom correspondence should be addressed: [email protected]. Tel.: +1 540 3156512
Physics of Fluids 33, 073603 (2021)
Article history
Received:
April 16 2021
Accepted:
June 01 2021
Citation
Han Gao, Luning Sun, Jian-Xun Wang; Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels. Physics of Fluids 1 July 2021; 33 (7): 073603. https://doi.org/10.1063/5.0054312
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