Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
Abstract
In many situations across computational science and engineering, multiple computational models are available that describe a system of interest. These different models have varying evaluation costs and varying fidelities. Typically, a computationally expensive high-fidelity model describes the system with the accuracy required by the current application at hand, while lower-fidelity models are less accurate but computationally cheaper than the high-fidelity model. Outer-loop applications, such as optimization, inference, and uncertainty quantification, require multiple model evaluations at many different inputs, which often leads to computational demands that exceed available resources if only the high-fidelity model is used. This work surveys multifidelity methods that accelerate the solution of outer-loop applications by combining high-fidelity and low-fidelity model evaluations, where the low-fidelity evaluations arise from an explicit low-fidelity model (e.g., a simplified physics approximation, a reduced model, a data-fit surrogate) that approximates the same output quantity as the high-fidelity model. The overall premise of these multifidelity methods is that low-fidelity models are leveraged for speedup while the high-fidelity model is kept in the loop to establish accuracy and/or convergence guarantees. We categorize multifidelity methods according to three classes of strategies: adaptation, fusion, and filtering. The paper reviews multifidelity methods in the outer-loop contexts of uncertaintymore »
- Authors:
-
- Univ. of Wisconsin, Madison, WI (United States)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Florida State Univ., Tallahassee, FL (United States)
- Publication Date:
- Research Org.:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Florida State Univ., Tallahassee, FL (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1500214
- Grant/Contract Number:
- SC0009297; SC0009324
- Resource Type:
- Accepted Manuscript
- Journal Name:
- SIAM Review
- Additional Journal Information:
- Journal Volume: 60; Journal Issue: 3; Journal ID: ISSN 0036-1445
- Publisher:
- Society for Industrial and Applied Mathematics
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Peherstorfer, Benjamin, Willcox, Karen, and Gunzburger, Max. Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization. United States: N. p., 2018.
Web. doi:10.1137/16m1082469.
Peherstorfer, Benjamin, Willcox, Karen, & Gunzburger, Max. Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization. United States. https://doi.org/10.1137/16m1082469
Peherstorfer, Benjamin, Willcox, Karen, and Gunzburger, Max. Wed .
"Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization". United States. https://doi.org/10.1137/16m1082469. https://www.osti.gov/servlets/purl/1500214.
@article{osti_1500214,
title = {Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization},
author = {Peherstorfer, Benjamin and Willcox, Karen and Gunzburger, Max},
abstractNote = {In many situations across computational science and engineering, multiple computational models are available that describe a system of interest. These different models have varying evaluation costs and varying fidelities. Typically, a computationally expensive high-fidelity model describes the system with the accuracy required by the current application at hand, while lower-fidelity models are less accurate but computationally cheaper than the high-fidelity model. Outer-loop applications, such as optimization, inference, and uncertainty quantification, require multiple model evaluations at many different inputs, which often leads to computational demands that exceed available resources if only the high-fidelity model is used. This work surveys multifidelity methods that accelerate the solution of outer-loop applications by combining high-fidelity and low-fidelity model evaluations, where the low-fidelity evaluations arise from an explicit low-fidelity model (e.g., a simplified physics approximation, a reduced model, a data-fit surrogate) that approximates the same output quantity as the high-fidelity model. The overall premise of these multifidelity methods is that low-fidelity models are leveraged for speedup while the high-fidelity model is kept in the loop to establish accuracy and/or convergence guarantees. We categorize multifidelity methods according to three classes of strategies: adaptation, fusion, and filtering. The paper reviews multifidelity methods in the outer-loop contexts of uncertainty propagation, inference, and optimization.},
doi = {10.1137/16m1082469},
journal = {SIAM Review},
number = 3,
volume = 60,
place = {United States},
year = {Wed Aug 08 00:00:00 EDT 2018},
month = {Wed Aug 08 00:00:00 EDT 2018}
}
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