A generalized hierarchical co-Kriging model for multi-fidelity data fusion

Zhou, Qi; Wu, Yuda; Guo, Zhendong; Hu, Jiexiang; Jin, Peng

doi:10.1007/s00158-020-02583-7

Cited by 48 publications

(15 citation statements)

References 65 publications

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“…In co-Kriging, the metamodel is built with two sets of data, one set named expensive data ( e , y e ) that are more computationally costly, while the other set is called cheap data ( c , y c ) (Zhou et al 2020). These two sets of data are independent of each other.…”

Section: Co-krigingmentioning

confidence: 99%

Lightweight design of variable-angle filament-wound cylinders combining Kriging-based metamodels with particle swarm optimization

Wang

Almeida

Ashok

et al. 2022

Struct Multidisc Optim

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Variable-angle filament-wound (VAFW) cylinders are herein optimized for minimum mass under manufacturing constraints, and for various design loads. A design parameterization based on a second-order polynomial variation of the tow winding angle along the axial direction of the cylinders is utilized to explore the nonlinear steering-thickness dependency in VAFW structures, whereby the thickness becomes a function of the filament steering angle. Particle swarm optimization coupled with three Kriging-based metamodels is used to find the optimum designs. A single-curvature Bogner–Fox–Schmit–Castro finite element is formulated to accurately and efficiently represent the variable stiffness properties of the shells, and verifications are performed using a general purpose plate element. Alongside the main optimization studies, a vast analysis of the design space is performed using the metamodels, showing a gap in the design space for the buckling strength that is confirmed by genetic algorithm optimizations. Extreme lightweight while buckling-resistant designs are reached, along with non-conventional optimum layouts thanks to the high degree of thickness build-up tailoring.

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Section: Co-krigingmentioning

confidence: 99%

Lightweight design of variable-angle filament-wound cylinders combining Kriging-based metamodels with particle swarm optimization

Wang

Almeida

Ashok

et al. 2022

Struct Multidisc Optim

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“…Trained surrogate models are used in lieu of computationally expensive simulation models when rapid reactions are required. We now introduce the basic theory of a commonly used surrogate modeling approach in ASO: kriging (also known as Gaussian process regression) [29,[147][148][149][150].…”

Section: Traditional Surrogate Modelsmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

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Large volumes of experimental and simulation aerodynamic data have been rapidly advancing aerodynamic shape optimization (ASO) via machine learning (ML), whose effectiveness has been growing thanks to continued developments in deep learning. In this review, we first introduce the state of the art and the unsolved challenges in ASO. Next, we present a description of ML fundamentals and detail the ML algorithms that have succeeded in ASO. Then we review ML applications contributing to ASO from three fundamental perspectives: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands like interactive design optimization. However, practical large-scale design optimizations remain a challenge due to the costly ML training expense. A deep coupling of ML model construction with ASO prior experience and knowledge, such as taking physics into account, is recommended to train ML models effectively.

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“…One alternative that has captivated interest in the field of MF modelling is metamodelling. It consists in creating surrogates of a function or model by characterizing the relationship between inputs and outputs and relies on metamodels as LF pairs to HF data [6]. An overview of popular metamodelling techniques is provided in [7], and [8] provides insight on the usage of MF paired with metamodelling.…”

Section: Forms Of Multi-fidelity Modellingmentioning

confidence: 99%

The role of multi-fidelity modelling in adaptation and recovery of engineering systems

Teixeira

Martinez-Pastor

Nogal

et al. 2022

APP

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Significant research has been conducted in identifying optimal recovery and adaptation decisions in disruptive scenarios using engineering models. In this context, an aspect that has been target of limited research is that of response times. Modelling is expected to grow progressively more complex as it becomes more accurate. Such complexity increases modelling efforts, and the promise of optimal adaptation and recovery may become hindered. The present work discusses the role of modelling fidelities in adaptation and recovery of systems, and in particular that of using a lower fidelity model that enables zero-time analyses of a system. A framework is proposed for using different fidelities in adaptation and recovery, considering system’s decision time requirements. The relevance of this analysis is researched in two traffic networks and results show that multi-fidelity models should be expected to play a key role in increasing the efficiency of optimal adaptation and recovery decisions.

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A generalized hierarchical co-Kriging model for multi-fidelity data fusion

Cited by 48 publications

References 65 publications

Lightweight design of variable-angle filament-wound cylinders combining Kriging-based metamodels with particle swarm optimization

Lightweight design of variable-angle filament-wound cylinders combining Kriging-based metamodels with particle swarm optimization

Machine Learning in Aerodynamic Shape Optimization

The role of multi-fidelity modelling in adaptation and recovery of engineering systems

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