A Triple Model Reduction for Data-Driven Large-Scale Inverse Problems in High Dimensional Parameter Spaces
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About this item
| Description: |
Bui-Thanh, T
Monday 5th March 2018 - 14:45 to 15:30 |
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| Created: | 2018-03-14 17:27 |
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| Collection: | Uncertainty quantification for complex systems: theory and methodologies |
| Publisher: | Isaac Newton Institute |
| Copyright: | Bui-Thanh, T |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-authors: Ellen Le (The University of Texas At Austin), Aaron Myers (The University of Texas At Austin), Brad Marvin (The University of Texas At Austin), Vishwas Rao (Argone National Lab)
We present an approach to address the challenge of data-driven large-scale inverse problems in high dimensional parameter spaces. The idea is to combine a goal-oriented model reduction approach for state, data-informed/active-subspace reduction for parameter, and randomized misfit approach for data reduction. The method is designed to mitigate the bottle neck of large-scale PDE solve, of high dimensional parameter space exploration, and of ever-increasing volume of data. Various theoretical and numerical results will be presented to support the proposed approach. |
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