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| + | ==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> | ||
Latest revision as of 09:32, 26 May 2023
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
Document information
Published on 26/05/23
Submitted on 26/05/23
Volume Adaptive Methods for Surrogate and Reduced Order Modeling, 2023
DOI: 10.23967/admos.2023.009
Licence: CC BY-NC-SA license
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