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Machine Learning Techniques for Data Reduction of CFD Applications
April 30, 2024, 4:42 a.m. | Jaemoon Lee, Ki Sung Jung, Qian Gong, Xiao Li, Scott Klasky, Jacqueline Chen, Anand Rangarajan, Sanjay Ranka
cs.LG updates on arXiv.org arxiv.org
Abstract: We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics (CFD) and other scientific applications. It uses a multidimensional block of tensors (spanning in space and time) for both input and output, capturing the spatiotemporal and interspecies relationship within a tensor. The tensor consists of species that represent different elements in a CFD simulation. To guarantee the error bound of the reconstructed …
abstract applications arxiv autoencoder block cfd computational correlations cs.lg data data reduction dynamics fluid dynamics generated machine machine learning machine learning techniques multidimensional scientific space space and time tensor type
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