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Title: 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
ORCiD logo [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

Abstract 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. 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 Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
US Air Force Office of Scientific Research (AFOSR); 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, Vol. 123, Issue 12; ISSN 0029-5981
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English

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