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Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems

Journal Article · · International Journal for Numerical Methods in Engineering
DOI:https://doi.org/10.1002/nme.6958· OSTI ID:1855808
 [1];  [2];  [1];  [3];  [4];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Texas A & M Univ., College Station, TX (United States)
  3. Consiglio Nazionale delle Ricerche Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes” (CNR‐IMATI), Pavia (Italy)
  4. Univ. of Michigan, Ann Arbor, MI (United States)
+ Show Author Affiliations
We present an adaptive algorithm for constructing surrogate models of multi-disciplinary systems composed of a set of coupled components. With this goal we introduce “coupling” variables with a priori unknown distributions that allow surrogates of each component to be built independently. Once built, the surrogates of the components are combined to form an integrated-surrogate that can be used to predict system-level quantities of interest at a fraction of the cost of the original model. The error in the integrated-surrogate is greedily minimized using an experimental design procedure that allocates the amount of training data, used to construct each component-surrogate, based on the contribution of those surrogates to the error of the integrated-surrogate. Here, the multi-fidelity procedure presented is a generalization of multi-index stochastic collocation that can leverage ensembles of models of varying cost and accuracy, for one or more components, to reduce the computational cost of constructing the integrated-surrogate. Extensive numerical results demonstrate that, for a fixed computational budget, our algorithm is able to produce surrogates that are orders of magnitude more accurate than methods that treat the integrated system as a black-box.
Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
Air Force Office of Scientific Research; USDOE; USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
NA0003525
OSTI ID:
1855808
Alternate ID(s):
OSTI ID: 1856643
Report Number(s):
SAND2022-2695J; 703989
Journal Information:
International Journal for Numerical Methods in Engineering, Journal Name: International Journal for Numerical Methods in Engineering Journal Issue: 12 Vol. 123; ISSN 0029-5981
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

42 ENGINEERING
Uncertainty quantification
dimension reduction
experimental design
integrated systems
multi-disciplinary
multi-fidelity
multi-physics
surrogate