Inpainting Computational Fluid Dynamics with Deep Learning

• URL: http://arxiv.org/abs/2402.17185v1
• Date: Tue, 27 Feb 2024 03:44:55 GMT
• Title: Inpainting Computational Fluid Dynamics with Deep Learning
• Authors: Dule Shu, Wilson Zhen, Zijie Li, Amir Barati Farimani
• Abstract summary: An effective fluid data completion method reduces the required number of sensors in a fluid dynamics experiment. The ill-posed nature of the fluid data completion problem makes it prohibitively difficult to obtain a theoretical solution. We employ the vector quantization technique to map both complete and incomplete fluid data spaces onto discrete-valued lower-dimensional representations.
• Score: 8.397730500554047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fluid data completion is a research problem with high potential benefit for both experimental and computational fluid dynamics. An effective fluid data completion method reduces the required number of sensors in a fluid dynamics experiment, and allows a coarser and more adaptive mesh for a Computational Fluid Dynamics (CFD) simulation. However, the ill-posed nature of the fluid data completion problem makes it prohibitively difficult to obtain a theoretical solution and presents high numerical uncertainty and instability for a data-driven approach (e.g., a neural network model). To address these challenges, we leverage recent advancements in computer vision, employing the vector quantization technique to map both complete and incomplete fluid data spaces onto discrete-valued lower-dimensional representations via a two-stage learning procedure. We demonstrated the effectiveness of our approach on Kolmogorov flow data (Reynolds number: 1000) occluded by masks of different size and arrangement. Experimental results show that our proposed model consistently outperforms benchmark models under different occlusion settings in terms of point-wise reconstruction accuracy as well as turbulent energy spectrum and vorticity distribution.

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