@inproceedings {pub3961,
	title = {A compact spectral descriptor for shape deformations},
	author = {Skylar Sible AND Rodrigo Iza-Teran AND Jochen Garcke AND Nikola Aulig AND Patricia Wollstadt},
	year = {2020},
	month = {September},
	abstract = {Modern product development cycles are increasingly driven by the computational analysis of digital simulation models. Efficient simulation requires a suitable representation of the component or structure under development--typically a polygon surface mesh--, as well as an efficient representation of critical design criteria. In various application domains, one such criterion is the plastic deformation of a part under stress, which is commonly studied by measuring node displacement in the surface mesh. This approach limits the computational analysis of deformation behavior to relatively simple deformation modes and typically requires visual inspections of the data making the design process costly. In this work, we propose a novel representation of deformation behavior using a compact descriptor based on spectral mesh processing. The descriptor approximates a geometric deformation by a set of spectral components, selected in a targeted fashion. The resulting descriptor is orders of magnitude smaller than the mesh representation in the spatial domain while preserving a high amount of geometric information relevant for the characterization of the deformation behavior. We demonstrate that the descriptor may be used to efficiently filter simulation results by deformation mode, which suggests its potential for application in further design tasks like structural optimization.},
	publisher = {IOS Press},
	booktitle = {24th European Conference on Artificial Intelligence (ECAI 2020)},
	volume = {325},
	pages = {1930 - 1937},
	series = {Frontiers in Artificial Intelligence and Applications}
}