On generative models as the basis for digital twins

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2021-08-31 DOI:10.1017/dce.2021.13
G. Tsialiamanis, D. Wagg, N. Dervilis, K. Worden
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引用次数: 8

Abstract

Abstract A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modeling applications. Two different types of generative models are considered here. The first is a physics-based model based on the stochastic finite element (SFE) method, which is widely used when modeling structures that have material and loading uncertainties imposed. Such models can be calibrated according to data from the structure and would be expected to outperform any other model if the modeling accurately captures the true underlying physics of the structure. The potential use of SFE models as digital mirrors is illustrated via application to a linear structure with stochastic material properties. For situations where the physical formulation of such models does not suffice, a data-driven framework is proposed, using machine learning and conditional generative adversarial networks (cGANs). The latter algorithm is used to learn the distribution of the quantity of interest in a structure with material nonlinearities and uncertainties. For the examples considered in this work, the data-driven cGANs model outperforms the physics-based approach. Finally, an example is shown where the two methods are coupled such that a hybrid model approach is demonstrated.
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论作为数字孪生基础的生成模型
摘要提出了一种生成模型框架,作为数字孪生或结构镜像的基础。该建议是基于确定性模型不能解释大多数结构建模应用中存在的不确定性的前提。这里考虑了两种不同类型的生成模型。第一种是基于随机有限元(SFE)方法的基于物理的模型,该模型广泛用于对具有材料和载荷不确定性的结构进行建模。这样的模型可以根据来自结构的数据进行校准,并且如果建模准确地捕获了结构的真实潜在物理特性,则有望优于任何其他模型。通过对具有随机材料特性的线性结构的应用,说明了SFE模型作为数字反射镜的潜在用途。对于这些模型的物理公式不能满足的情况,提出了一个数据驱动的框架,使用机器学习和条件生成对抗网络(cgan)。后一种算法用于学习具有材料非线性和不确定性的结构中感兴趣量的分布。对于本工作中考虑的示例,数据驱动的cgan模型优于基于物理的方法。最后,给出了一个示例,其中两种方法耦合在一起,从而演示了混合模型方法。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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