Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems
Fixed point iteration is a common strategy to handle interdisciplinary coupling within a feedback-coupled multidisciplinary analysis. For each coupled analysis, this requires a large number of disciplinary high-fidelity simulations to resolve the interactions between different disciplines. When embe...
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| Format: | Article |
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American Institute of Aeronautics and Astronautics (AIAA)
2018
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| Online Access: | http://hdl.handle.net/1721.1/117036 https://orcid.org/0000-0002-2281-3067 https://orcid.org/0000-0003-4222-5358 https://orcid.org/0000-0003-2156-9338 |
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| author | Chaudhuri, Anirban Lam, Remi Willcox, Karen E |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Chaudhuri, Anirban Lam, Remi Willcox, Karen E |
| author_sort | Chaudhuri, Anirban |
| collection | MIT |
| description | Fixed point iteration is a common strategy to handle interdisciplinary coupling within a feedback-coupled multidisciplinary analysis. For each coupled analysis, this requires a large number of disciplinary high-fidelity simulations to resolve the interactions between different disciplines. When embedded within an uncertainty analysis loop (e.g., with Monte Carlo sampling over uncertain parameters), the number of high-fidelity disciplinary simulations quickly becomes prohibitive, because each sample requires a fixed point iteration and the uncertainty analysis typically involves thousands or even millions of samples. This paper develops a method for uncertainty quantification in feedback-coupled systems that leverage adaptive surrogates to reduce the number of cases forwhichfixedpoint iteration is needed. The multifidelity coupled uncertainty propagation method is an iterative process that uses surrogates for approximating the coupling variables and adaptive sampling strategies to refine the surrogates. The adaptive sampling strategies explored in this work are residual error, information gain, and weighted information gain. The surrogate models are adapted in a way that does not compromise the accuracy of the uncertainty analysis relative to the original coupled high-fidelity problem as shown through a rigorous convergence analysis. |
| first_indexed | 2024-09-23T14:11:17Z |
| format | Article |
| id | mit-1721.1/117036 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:11:17Z |
| publishDate | 2018 |
| publisher | American Institute of Aeronautics and Astronautics (AIAA) |
| record_format | dspace |
| spelling | mit-1721.1/1170362022-10-01T19:43:21Z Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems Chaudhuri, Anirban Lam, Remi Willcox, Karen E Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Chaudhuri, Anirban Lam, Remi Willcox, Karen E Fixed point iteration is a common strategy to handle interdisciplinary coupling within a feedback-coupled multidisciplinary analysis. For each coupled analysis, this requires a large number of disciplinary high-fidelity simulations to resolve the interactions between different disciplines. When embedded within an uncertainty analysis loop (e.g., with Monte Carlo sampling over uncertain parameters), the number of high-fidelity disciplinary simulations quickly becomes prohibitive, because each sample requires a fixed point iteration and the uncertainty analysis typically involves thousands or even millions of samples. This paper develops a method for uncertainty quantification in feedback-coupled systems that leverage adaptive surrogates to reduce the number of cases forwhichfixedpoint iteration is needed. The multifidelity coupled uncertainty propagation method is an iterative process that uses surrogates for approximating the coupling variables and adaptive sampling strategies to refine the surrogates. The adaptive sampling strategies explored in this work are residual error, information gain, and weighted information gain. The surrogate models are adapted in a way that does not compromise the accuracy of the uncertainty analysis relative to the original coupled high-fidelity problem as shown through a rigorous convergence analysis. United States. Army Research Office. Multidisciplinary University Research Initiative (Award FA9550-15-1-0038) 2018-07-20T19:33:04Z 2018-07-20T19:33:04Z 2017-08 2018-04-17T14:25:03Z Article http://purl.org/eprint/type/ConferencePaper 0001-1452 1533-385X http://hdl.handle.net/1721.1/117036 Chaudhuri, Anirban, et al. “Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems.” AIAA Journal, vol. 56, no. 1, Jan. 2018, pp. 235–49. © 2017 by Anirban Chaudhuri, Remi Lam, and Karen Willcox https://orcid.org/0000-0002-2281-3067 https://orcid.org/0000-0003-4222-5358 https://orcid.org/0000-0003-2156-9338 http://dx.doi.org/10.2514/1.J055678 AIAA Journal Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Institute of Aeronautics and Astronautics (AIAA) MIT Web Domain |
| spellingShingle | Chaudhuri, Anirban Lam, Remi Willcox, Karen E Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems |
| title | Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems |
| title_full | Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems |
| title_fullStr | Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems |
| title_full_unstemmed | Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems |
| title_short | Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems |
| title_sort | multifidelity uncertainty propagation via adaptive surrogates in coupled multidisciplinary systems |
| url | http://hdl.handle.net/1721.1/117036 https://orcid.org/0000-0002-2281-3067 https://orcid.org/0000-0003-4222-5358 https://orcid.org/0000-0003-2156-9338 |
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