Recent advances and applications of surrogate models for finite element method computations: a review

Kůdela, Jakub; Matoušek, Radomil

doi:10.1007/s00500-022-07362-8

Cited by 102 publications

(31 citation statements)

References 155 publications

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“…Although the current study reveals the importance of considering using stochastic models to study the mechanics of the middle ear, these models require a significantly higher computational cost in comparison to conventional deterministic FE models. One way to deal with the high computational cost is to develop and train surrogate models 51 that can be used in lieu of the real FE model for stochastic simulations for some specific output quantities of interest. Future studies should study development and effectiveness of such surrogate models.…”

Section: Discussionmentioning

confidence: 99%

Inaccuracies of deterministic finite-element models of human middle ear revealed by stochastic modelling

Ebrahimian

Mohammadi

Rosowski

et al. 2023

Sci Rep

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For over 40 years, finite-element models of the mechanics of the middle ear have been mostly deterministic in nature. Deterministic models do not take into account the effects of inter-individual variabilities on middle-ear parameters. We present a stochastic finite-element model of the human middle ear that uses variability in the model parameters to investigate the uncertainty in the model outputs (umbo, stapes, and tympanic-membrane displacements). We demonstrate: (1) uncertainties in the model parameters can be magnified by more than three times in the umbo and stapes footplate responses at frequencies above 2 kHz; (2) middle-ear models are biased and they distort the output distributions; and (3) with increased frequency, the highly-uncertain regions spatially spread out on the tympanic membrane surface. Our results assert that we should be mindful when using deterministic finite-element middle-ear models for critical tasks such as novel device developments and diagnosis.

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Section: Discussionmentioning

confidence: 99%

Inaccuracies of deterministic finite-element models of human middle ear revealed by stochastic modelling

Ebrahimian

Mohammadi

Rosowski

et al. 2023

Sci Rep

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“…Radial Basin Functions and more recently Machine and Deep Learning approaches. More detailed insights and a summary of the state of the art in this domain can be found in (Kudela andMatousek 2022, Koeppe 2021). At this stage, it is not possible to recommend a general method for the formation of the surrogate model.…”

Section: Iced23mentioning

confidence: 99%

“…Despite the given limitations, recent studies have shown not only on theoretical examples but also on industrial and practical applications that surrogate models can be efficiently used to analyse a system with variable influence parameters. (Kudela andMatousek 2022, Koeppe 2021) The analysis of the state of the art reveals promising approaches and methods that can be used to address the challenges in DU optimization. As a result, a methodological approach based on these impulses is proposed in Section 3, which is subsequently applied to the DU in Section 4.…”

Section: Iced23mentioning

confidence: 99%

Systematic Optimisation Process for an Ebike Drive Unit in a Highly Variable Environment

Steck

Husung

2023

Proc. Des. Soc.

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Drive units of eBikes are used in every type of bicycle and for different riding scenarios and riders. Due to the different riders and bike types, an enormous variety of influencing parameters and load spectra must be considered during the design process. Therefore, in this paper, a systematic approach for the optimization of the drive unit is presented, which adopts and combines several approaches from design theory. The focus is on efficient modeling and simulation of the relevant parameters and load spectra to minimize uncertainties in the design process.Based on a system analysis, dimension-reduced parameter spaces are formed for the simulation of the system, meta-models are integrated into the simulation model and the results of the simulation are transferred into a data-based surrogate model to cover the parameter space in an efficient way with a minimum number of time consuming FE simulations. Furthermore, a coordinate-based evaluation method is presented for the FE model in order to form the input for the surrogate model, reduces the amount of data, and to allows a geometry- and mesh-independent evaluation to compare different models.

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“…It is then a very attractive and active area of research the development of less expensive alternative numerical models. Soft computing techniques, and neural networks in particular, have proven as powerful and reliable methods to create computationally efficient data-driven surrogate models (Kudela and Matousek, 2022). These type of surrogate models are able to approximate the results of an expensive model with only a fraction of the computational cost.…”

Section: Fe Surrogate Modelsmentioning

confidence: 99%

Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps

Solorzano

Plevris

2022

Front. Built Environ.

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The modeling and simulation of structural systems is a task that requires high precision and reliable results to ensure the stability and safety of construction projects of all kinds. For many years now, structural engineers have relied on hard computing strategies for solving engineering problems, such as the application of the Finite Element Method (FEM) for structural analysis. However, despite the great success of FEM, as the complexity and difficulty of modern constructions increases, the numerical procedures required for their appropriated design become much harder to process using traditional methods. Therefore, other alternatives such as Computational Intelligence (CI) techniques are gaining substantial popularity among professionals and researchers in the field. In this study, a data-driven bibliometric analysis is presented with the aim to investigate the current research directions and the applications of CI-based methodologies for the simulation and modeling of structures. The presented study is centered on a self-mined database of nearly 8000 publications from 1990 to 2022 with topics related to the aforementioned field. The database is processed to create various two-dimensional bibliometric maps and analyze the relevant research metrics. From the maps, some of the trending topics and research gaps are identified based on an analysis of the keywords. Similarly, the most contributing authors and their collaborations are assessed through an analysis of the corresponding citations. Finally, based on the discovered research directions, various recent publications are selected from the literature and discussed in detail to set examples of innovative CI-based applications for the modeling and simulation of structures. The full methodology that is used to obtain the data and generate the bibliometric maps is presented in detail as a means to provide a clearer interpretation of the bibliometric analysis results.

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Recent advances and applications of surrogate models for finite element method computations: a review

Cited by 102 publications

References 155 publications

Inaccuracies of deterministic finite-element models of human middle ear revealed by stochastic modelling

Inaccuracies of deterministic finite-element models of human middle ear revealed by stochastic modelling

Systematic Optimisation Process for an Ebike Drive Unit in a Highly Variable Environment

Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps

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