Approximation and Calibration of Nonlinear Structural Dynamics

Zimmerman, D. C.; Hasselman, T. K.; Anderson, Mark C.

doi:10.1007/s11071-005-1917-x

Cited by 9 publications

(7 citation statements)

References 4 publications

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“…Due to a prerequisite library of models, the 'updated' model is, however, limited to a discrete set of models that are manually defined by the user, and consequently is of limited accuracy. Other MLbased methodologies aim at finding, online, the (global) parameter values corresponding to a measured signal (in a continuous domain) using, for example, iterative genetic algorithms [58,59]. In this case, however, the (genetic) search over the parameter space takes considerable computational effort, rendering real-time updating impossible [38].…”

Section: Introductionmentioning

confidence: 99%

Real-time parameter updating for nonlinear digital twins using inverse mapping models and transient-based features

2023

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In the context of digital twins, it is essential that a model gives an accurate description of the (controlled) dynamic behavior of a physical system during the system’s entire operational life. Therefore, model updating techniques are required that enable real-time updating of physically interpretable parameter values and are applicable to a wide range of (nonlinear) dynamical systems. As traditional, iterative, parameter updating methods may be computationally too expensive for real-time updating, the inverse mapping parameter updating (IMPU) method is proposed as an alternative. For this method, first, an artificial neural network (ANN) is trained offline using novel features of simulated transient response data. Then, in the online phase, this ANN maps, with little computational cost, a set of measured output response features to parameter estimates enabling real-time model updating. In this paper, various types of transient response features are introduced to update parameter values of nonlinear dynamical systems with increased computational efficiency and accuracy. To analyze the efficacy of these features, the IMPU method is applied to a (simulated) nonlinear multibody system. It is shown that a smart selection of features, based on, e.g., the frequency content of the transient response, can improve the accuracy of the estimated parameter values, leading to more accurate updated models. Furthermore, the generalization capabilities of the ANNs are analyzed for these feature types, by varying the number of training samples and assessing the effect of incomplete training data. It is shown that the IMPU method can predict parameter values that are not part of the training data with acceptable accuracy as well.

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Section: Introductionmentioning

confidence: 99%

Real-time parameter updating for nonlinear digital twins using inverse mapping models and transient-based features

2023

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“…These ANNs are renowned for their universal function approximation capabilities [54]. Moreover, in the online phase, inferring parameter values using ANNs is computationally efficient, as is also discussed in [51], where ANNs are used as surrogates for first-principles models.…”

Section: Inverse Mapping Modelmentioning

confidence: 99%

“…Note that, since other generic function approximation algorithms may also be used to constitute IMMs, the authors prefer to refer to this methodology as the Inverse Mapping Parameter Updating (IMPU) method. For example, Zimmerman uses genetic algorithms [51]. Moreover, in recent work of the authors, Gaussian processes are used as IMMs, with the added benefit of enabling uncertainty quantification for the estimated parameter values [52].…”

Section: Introductionmentioning

confidence: 99%

Real-time parameter updating for nonlinear digital twins using inverse mapping models and transient-based features

Kessels

Fey

Wouw

2022

Preprint

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In the context of digital twins, it is essential that a model gives an accurate description of the (controlled) dynamic behavior of a physical system during the system’s entire operational life. Therefore, model updating techniques are required that enable real-time updating of physically interpretable parameter values and are applicable to a wide range of (nonlinear) dynamical systems. As traditional, iterative, parameter updating methods may be computationally too expensive for real-time updating, the Inverse Mapping Parameter Updating (IMPU) method is proposed as an alternative. For this method, first, an Artificial Neural Network (ANN) is trained offline using novel features of simulated transient response data. Then, in the online phase, this ANN maps, with little computational cost, a set of measured output response features to parameter estimates enabling real-time model updating. In this paper, various types of transient response features are introduced to update parameter values of nonlinear dynamical systems with increased computational efficiency and accuracy. To analyze the efficacy of these features, the IMPU method is applied to a (simulated) nonlinear multibody system. It is shown that a smart selection of features, based on, e.g., the frequency content of the transient response, can improve the accuracy of the estimated parameter values, leading to more accurate updated models. Furthermore, the generalization capabilities of the ANNs are analyzed for these feature types, by varying the number of training samples and assessing the effect of incomplete training data. It is shown that the IMPU method can predict parameter values that are not part of the training data with acceptable accuracy as well.

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“…Waszczyszyn and Ziemiański [110], for example, used several different NN architectures and evaluated their efficacy in several physical problems, such as calculating vibrational properties of buildings, and damage detection in beams. ANNs have also been used in structural dynamics [111,112] Another good example of material characterization is nanofluids: fluids that contain solid particles of nanometer-sized characteristic dimensions. Properties of nanofluids, such as thermal conductivity and viscosity, are complex functions of many different parameters, including temperature, particle size, and particle aggregation.…”

Section: Surrogate Modellingmentioning

confidence: 99%

Machine-Learning Methods for Computational Science and Engineering

2020

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The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.

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Approximation and Calibration of Nonlinear Structural Dynamics

Cited by 9 publications

References 4 publications

Real-time parameter updating for nonlinear digital twins using inverse mapping models and transient-based features

Real-time parameter updating for nonlinear digital twins using inverse mapping models and transient-based features

Real-time parameter updating for nonlinear digital twins using inverse mapping models and transient-based features

Machine-Learning Methods for Computational Science and Engineering

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