Original paper

A pruning algorithm preserving modeling capabilities for polycrystalline data

Volume: 68, Issue: 6, Pages: 1407 - 1419
Published: Sep 25, 2021
Abstract
We are exploring the idea of data pruning via hyperreduction modeling. The main novelty of this paper is a lossy data compression/decompression approach for ploycrystalline data, which is based on a hyperreduction scheme that preserves data driven modeling capabilities after compression. We assume to know a mechanical model whose equations are satisfied by the data. It is shown that the proposed reconstruction of the data performs an oblique...
Paper Details
Title
A pruning algorithm preserving modeling capabilities for polycrystalline data
Published Date
Sep 25, 2021
Volume
68
Issue
6
Pages
1407 - 1419
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