Multifidelity Uncertainty Propagation in Coupled Multidisciplinary Systems

Chaudhuri, Anirban; Willcox, Karen

doi:10.2514/6.2016-1442

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…However, a new method is proposed that leverages adaptive surrogates for uncertainty analysis in black-box systems. 179 Other methods have solved the feedback-coupling problem by decoupling the system. However, when sensitivity to coupling variables is high, such approaches might produce poor results.…”

Section: Uncertainty-based Multidisciplinary Design and Optimizationmentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. Since technology is evaluated in the conceptual design phase and little is known about the technology, large uncertainty is present. This uncertainty needs to be accurately assessed and managed. To investigate the research efforts that have been performed to perform technology evaluation under uncertainty, a literature review was conducted, focusing on methods and modeling approaches to assign and quantify these uncertainties. It is found that probability theory is still the most popular theory for representing uncertainty. Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used. Commonly, surrogate models are used to reduce computational effort. Other efforts focus on the use of multifidelity approaches to reduce computational effort when high-fidelity methods are required. Four issues that may need to be addressed in future research were identified.

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Section: Uncertainty-based Multidisciplinary Design and Optimizationmentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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“…Reference Dimensionality [56] 2D/3D Eu, [81] 1D/3D RANS+TM, [84] 2D/3D URANS, [107] 2D/3D, [145] 1D/2D RANS, [156] 1D/2D Li, [175] 1D/3D RANS, [187] 1D/3D RANS, [203] 1D,2D/3D RANS Coarse/Refined [5] Eu, [25] RANS, [34] Eu, [35] Eu, [36] Li/Eu, [82] RANS, [88] MFF, [91] MHD, [98] Eu, [99], Eu [102] Eu, [115] Eu, [119] RANS, [153] RANS, [170] RANS, [196] [12] Li, [21] NL, [23] Li, [24] NL, [31] Li, [113] Li, [122] Li, [166] NL, [167] NL, [186] Li, [194] Li, [195] Li Boundary Conditions [182] Li, [185] Li [32] employed an iterative method that used LF surrogate models for approximating coupling variables and adaptive sampling of the HF system to refine the surrogates in order to maintain a similar level of accuracy as uncertainty propagation using the coupled HF multidisciplinary system.…”

Section: Fluid Mechanics Fidelity Typementioning

confidence: 99%

Review of multi-fidelity models

Fernández-Godino¹

2016

Preprint

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Simulations are often computationally expensive and the need for multiple realizations, as in uncertainty quantification or optimization, makes surrogate models an attractive option. For expensive high-fidelity models (HFMs), however, even performing the number of simulations needed for fitting a surrogate may be too expensive. Inexpensive but less accurate low-fidelity models (LFMs) are often also available. Multi-fidelity models (MFMs) combine HFMs and LFMs in order to achieve accuracy at a reasonable cost. With the increasing popularity of MFMs in mind, the aim of this paper is to summarize the state-of-the-art of MFM trends. For this purpose, publications in this field are classified based on application, surrogate selection if any, the difference between fidelities, the method used to combine these fidelities, the field of application and the year published.Available methods of combining fidelities are also reviewed, focusing our attention especially on multi-fidelity surrogate models in which fidelities are combined inside a surrogate model. Computation time savings are usually the reason for using MFMs, hence it is important to properly report the achieved savings. Unfortunately, we find that many papers do not present sufficient information to determine these savings. Therefore, the paper also includes guidelines for authors to present their MFM savings in a way that is useful to future MFM users. Based on papers that provided enough information, we find that time savings are highly problem dependent and that MFM methods we surveyed provided time savings up to 90%.

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“…Heterogeneous surrogate models are also adopted to approximate fitness evaluations with different fidelity levels [5], [32], [34], [37], [46]. Moreover, in different fidelity levels, surrogate models can be built on different subsets of decision variables [47], [48], [49], in which separable decision variables are grouped into different subsets.…”

Section: Introductionmentioning

confidence: 99%

A Generic Test Suite for Evolutionary Multifidelity Optimization

Wang

Jin

Doherty

2018

IEEE Trans. Evol. Computat.

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Many real-world optimization problems involve computationally intensive numerical simulations to accurately evaluate the quality of solutions. Usually the fidelity of the simulations can be controlled using certain parameters and there is a trade-off between simulation fidelity and computational cost, i.e., the higher the fidelity, the more complex the simulation will be. To reduce the computational time in simulation-driven optimization, it is a common practice to use multiple fidelity levels in search for the optimal solution. So far, not much work has been done in evolutionary optimization that considers multiple fidelity levels in fitness evaluations. In this work, we aim to develop test suites that are able to capture some important characteristics in real-world multi-fidelity optimization, thereby offering a useful benchmark for developing evolutionary algorithms for multi-fidelity optimization. To demonstrate the usefulness of the proposed test suite, three strategies for adapting the fidelity level of the test problems during optimization are suggested and embedded in a particle swarm optimization algorithm. Our simulation results indicate that the use of changing fidelity is able to enhance the performance and reduce the computational cost of the particle swarm optimization, which is desired in solving expensive optimization problems.

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Multifidelity Uncertainty Propagation in Coupled Multidisciplinary Systems

Cited by 10 publications

References 23 publications

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Review of multi-fidelity models

A Generic Test Suite for Evolutionary Multifidelity Optimization

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