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Inpainting Computational Fluid Dynamics with Deep Learning
Feb. 28, 2024, 5:41 a.m. | Dule Shu, Wilson Zhen, Zijie Li, Amir Barati Farimani
cs.LG updates on arXiv.org arxiv.org
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 …
abstract arxiv benefit computational cs.lg data deep learning dynamics experiment experimental fluid dynamics inpainting mesh physics.flu-dyn research sensors type
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