Inpainting Computational Fluid Dynamics with Deep Learning
<|reference_start|>Inpainting Computational Fluid Dynamics with Deep Learning: 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.<|reference_end|>
@article{shu2024inpainting,
    title={Inpainting Computational Fluid Dynamics with Deep Learning},
    author={Dule Shu, Wilson Zhen, Zijie Li, Amir Barati Farimani},
    journal={arXiv preprint arXiv:2402.17185},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17185},
    primaryClass={cs.LG physics.flu-dyn}
}