Clustering-based Parametric Surrogate Modeling of Vibroacoustic Problems Assisted by Neural Networks and Active Subspace Method

                                
==Abstract==

This contribution presents a combined framework to perform parametric
surrogate modeling of vibroacoustic problems that enables efficient training of large-scale
problems. The proposed framework combines the active subspace method to perform
dimensionality reduction of high-dimensional problems and thereafter a clustering-based
approach within the identified active subspace region to yield smaller training clusters.
Finally, a trained neural network assists the cluster classification task for any desired
parameter point so as to query the parametric system response during the online phase.

== Full Paper ==
<pdf>Media:Draft_Sanchez Pinedo_13555365962_file.pdf</pdf>