Multifidelity Optimization using Statistical Surrogate Modeling for Non-Hierarchical Information Sources
Designing and optimizing complex systems often requires numerous evaluations of a quantity of interest. This is typically achieved by querying potentially expensive numerical models in an optimization process. To alleviate the cost of optimization, surrogate models can be used in lieu of the origina...
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| Format: | Article |
| Language: | en_US |
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American Institute of Aeronautics and Astronautics
2018
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| Online Access: | http://hdl.handle.net/1721.1/115996 https://orcid.org/0000-0003-4222-5358 https://orcid.org/0000-0003-2156-9338 |
| _version_ | 1826214223868329984 |
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| author | Allaire, Douglas 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 Allaire, Douglas Lam, Remi Willcox, Karen E |
| author_sort | Allaire, Douglas |
| collection | MIT |
| description | Designing and optimizing complex systems often requires numerous evaluations of a quantity of interest. This is typically achieved by querying potentially expensive numerical models in an optimization process. To alleviate the cost of optimization, surrogate models can be used in lieu of the original model, as they are cheaper to evaluate. In addition, different information sources with varying fidelity, such as numerical models, experimental results or historical data may be available to estimate the quantity of interest. This work proposes a strategy to adaptively construct and exploit a multifidelity surrogate when multiple information sources of varying fidelity are available. One of the distinguishing features of the proposed approach is the relaxation of the common assumption of hierarchical relationships among information sources. This is achieved by endowing the
surrogate representation with uncertainty functions that can vary across the design space; this uncertainty quantifies the fidelity of the underlying information source. The resulting multifidelity surrogate is used in an optimization setting to identify the next design to evaluate, as well as to select the information sources with which to perform the evaluation, based on information source evaluation cost and fidelity. For an aerodynamic design example, the proposed strategy leverages multifidelity information to reduce the number of evaluations of the expensive information source needed during the optimization. |
| first_indexed | 2024-09-23T16:02:04Z |
| format | Article |
| id | mit-1721.1/115996 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:02:04Z |
| publishDate | 2018 |
| publisher | American Institute of Aeronautics and Astronautics |
| record_format | dspace |
| spelling | mit-1721.1/1159962022-09-29T17:46:50Z Multifidelity Optimization using Statistical Surrogate Modeling for Non-Hierarchical Information Sources Allaire, Douglas Lam, Remi Willcox, Karen E Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Willcox, Karen L Lam, Remi Willcox, Karen E Designing and optimizing complex systems often requires numerous evaluations of a quantity of interest. This is typically achieved by querying potentially expensive numerical models in an optimization process. To alleviate the cost of optimization, surrogate models can be used in lieu of the original model, as they are cheaper to evaluate. In addition, different information sources with varying fidelity, such as numerical models, experimental results or historical data may be available to estimate the quantity of interest. This work proposes a strategy to adaptively construct and exploit a multifidelity surrogate when multiple information sources of varying fidelity are available. One of the distinguishing features of the proposed approach is the relaxation of the common assumption of hierarchical relationships among information sources. This is achieved by endowing the surrogate representation with uncertainty functions that can vary across the design space; this uncertainty quantifies the fidelity of the underlying information source. The resulting multifidelity surrogate is used in an optimization setting to identify the next design to evaluate, as well as to select the information sources with which to perform the evaluation, based on information source evaluation cost and fidelity. For an aerodynamic design example, the proposed strategy leverages multifidelity information to reduce the number of evaluations of the expensive information source needed during the optimization. United States. Air Force. Office of Scientific Research. Multidisciplinary University Research Initiative (Grant FA9550- 09-0613) Singapore-MIT Alliance Computational Engineering Programme 2018-05-30T19:30:59Z 2018-05-30T19:30:59Z 2015-01 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/115996 Lam, Rémi, et al. Multifidelity "Optimization Using Statistical Surrogate Modeling for Non-Hierarchical Information Sources." 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 5-9 January, 2015, Kissimmee, Florida, American Institute of Aeronautics and Astronautics, 2015. https://orcid.org/0000-0003-4222-5358 https://orcid.org/0000-0003-2156-9338 en_US https://doi.org/10.2514/6.2015-0143 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference Read More: https://arc.aiaa.org/doi/abs/10.2514/6.2015-0143 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Institute of Aeronautics and Astronautics Prof. Willcox via Barbara Williams |
| spellingShingle | Allaire, Douglas Lam, Remi Willcox, Karen E Multifidelity Optimization using Statistical Surrogate Modeling for Non-Hierarchical Information Sources |
| title | Multifidelity Optimization using Statistical Surrogate Modeling for Non-Hierarchical Information Sources |
| title_full | Multifidelity Optimization using Statistical Surrogate Modeling for Non-Hierarchical Information Sources |
| title_fullStr | Multifidelity Optimization using Statistical Surrogate Modeling for Non-Hierarchical Information Sources |
| title_full_unstemmed | Multifidelity Optimization using Statistical Surrogate Modeling for Non-Hierarchical Information Sources |
| title_short | Multifidelity Optimization using Statistical Surrogate Modeling for Non-Hierarchical Information Sources |
| title_sort | multifidelity optimization using statistical surrogate modeling for non hierarchical information sources |
| url | http://hdl.handle.net/1721.1/115996 https://orcid.org/0000-0003-4222-5358 https://orcid.org/0000-0003-2156-9338 |
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