Uncertainty quantification for partial differential equations: going beyond Monte Carlo
Duration: 52 mins 33 secs
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About this item
| Description: |
Gunzburger, M (Florida State University)
Tuesday 9th January 2018 - 10:00 to 11:00 |
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| Created: | 2018-01-10 16:17 |
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| Collection: | Uncertainty quantification for complex systems: theory and methodologies |
| Publisher: | Isaac Newton Institute |
| Copyright: | Gunzburger, M |
| 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: | We consider the determination of statistical information about outputs of interest that depend on the solution of a partial differential equation having random inputs, e.g., coefficients, boundary data, source term, etc. Monte Carlo methods are the most used approach used for this purpose. We discuss other approaches that, in some settings, incur far less computational costs. These include quasi-Monte Carlo, polynomial chaos, stochastic collocation, compressed sensing, reduced-order modeling, and multi-level and multi-fidelity methods for all of which we also discuss their relative strengths and weaknesses. |
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