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Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression. (arXiv:2208.07767v1 [cs.GR])
cs.LG updates on arXiv.org arxiv.org
Modern computational science and engineering applications are being improved
by the advances in scientific machine learning. Data-driven methods such as
Dynamic Mode Decomposition (DMD) can extract coherent structures from
spatio-temporal data generated from dynamical systems and infer different
scenarios for said systems. The spatio-temporal data comes as snapshots
containing spatial information for each time instant. In modern engineering
applications, the generation of high-dimensional snapshots can be time and/or
resource-demanding. In the present study, we consider two strategies for
enhancing DMD …
arxiv compression data data compression visualization workflow