AI RESEARCH
CA-Based Interpretable Knowledge Representation and Analysis of Geometric Design Parameters
arXiv CS.LG
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ArXi:2603.17535v1 Announce Type: new In many CAD-based applications, complex geometries are defined by a high number of design parameters. This leads to high-dimensional design spaces that are challenging for downstream engineering processes like simulations, optimization, and design exploration tasks. Therefore, dimension reduction methods such as principal component analysis (PCA) are used. The PCA identifies dominant modes of geometric variation and yields a compact representation of the geometry.