MODEL REDUCTION FOR LARGE-SCALE SYSTEMS WITH HIGH-DIMENSIONAL PARAMETRIC INPUT SPACE
A model-constrained adaptive sampling methodology is proposed for the reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a reduced basis approach, which requires the computation of high-fidelity solutions at a number of sample points throu...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Society for Industrial and Applied Mathematics
2010
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| Online Access: | http://hdl.handle.net/1721.1/52410 https://orcid.org/0000-0003-2156-9338 |
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Model Reduction for Large-Scale Systems with High Dimensional Parametric Input Space
Published 2010
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