Efficient Approximation of Coupling Variable Fixed Point Sets for Decoupling Multidisciplinary Systems

Isaac, Benson; Friedman, Samuel; Allaire, Douglas

doi:10.2514/6.2018-1908

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…Ref. [9] demonstrates empirically that the approximation error does not marginally improve with The resulting mean and standard deviation of the coupling variables are displayed in Table 3.3, while the statistics for the quantities of interest are shown in Table 3.4. The HDMR+IW method provided a good approximation to the truth model with a full order of magnitude decrease in the required number of full system evaluations.…”

Section: 1b Fire Detection Satellite Examplementioning

confidence: 99%

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Efficient Decoupling of Multiphysics Systems for Uncertainty Propagation

Friedman

2018

2018 AIAA Non-Deterministic Approaches Conference

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Uncertainty propagation through coupled multiphysics systems is often intractable due to computational expense. In this work, we present a novel methodology to enable uncertainty analysis of expensive coupled systems. The approach consists of offline discipline level analyses followed by an online synthesis that results in accurate approximations of full coupled system level uncertainty analyses. Coupling is handled by an efficient procedure for approximating the map from system inputs to fixed point sets that makes use of state of the art 1-minimization techniques and cut high dimensional model representations. The methodology is demonstrated on an analytic numerical example and a fire detection satellite system where it is shown to perform well as compared to brute force Monte Carlo simulation.

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Section: 1b Fire Detection Satellite Examplementioning

confidence: 99%

“…Following [9], we achieve this by building cut high dimensional model representations with only univariate and bivariate terms. To build these terms, we use 1 -minimization with a controllable error tolerance.…”

Section: Introductionmentioning

confidence: 99%

Efficient Decoupling of Multiphysics Systems for Uncertainty Propagation

Friedman

2018

2018 AIAA Non-Deterministic Approaches Conference

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“…The first class of methods build a single surrogate that maps all the system inputs

z

to all the coupling variables

ξ

25‐27 . The training data used to build the surrogate is obtained by evaluating the coupled system at realizations of the exogeneous variables and collecting the values of the coupling variables computed by FPI during each simulation.…”

Section: Introductionmentioning

confidence: 99%

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

Jakeman

Friedman

Eldred

et al. 2022

Numerical Meth Engineering

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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.

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Uncertainty Propagation for Multidisciplinary Problems

Brevault¹,

Balesdent²

2020

Springer Optimization and Its Applications

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Efficient Approximation of Coupling Variable Fixed Point Sets for Decoupling Multidisciplinary Systems

Cited by 3 publications

References 36 publications

Efficient Decoupling of Multiphysics Systems for Uncertainty Propagation

Efficient Decoupling of Multiphysics Systems for Uncertainty Propagation

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

Uncertainty Propagation for Multidisciplinary Problems

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