Web: http://arxiv.org/abs/2201.06210

Jan. 26, 2022, 2:11 a.m. | Wrik Mallik, Neil Farvolden, Jasmin Jelovica, Rajeev K. Jaiman

cs.LG updates on arXiv.org arxiv.org

This article presents a reduced-order modeling methodology via deep
convolutional neural networks (CNNs) for shape optimization applications. The
CNN provides a nonlinear mapping between the shapes and their associated
attributes while conserving the equivariance of these attributes to the shape
translations. To implicitly represent complex shapes via a CNN-applicable
Cartesian structured grid, a level-set method is employed. The CNN-based
reduced-order model (ROM) is constructed in a completely data-driven manner
thus well suited for non-intrusive applications. We demonstrate our ROM-based
shape …

arxiv convolutional neural network deep math network neural neural network optimization

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