用于工程系统估算和不确定性量化的以监测特征为条件的降序模型

Konstantinos Vlachas, Thomas Simpson, Anthony Garland, D. Dane Quinn, Charbel Farhat, Eleni Chatzi
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引用次数: 0

摘要

还原阶次模型(ROM)是工程领域的重要工具,因为它可以替代计算密集型数字孪生模拟器。尽管有纯数据驱动的 ROM 构建方法,但允许保留部分物理特性的方案往往会增强 ROM 的可解释性和通用性。然而,在处理具有参数依赖性特征的非线性系统时,基于物理的技术可能会对扩展产生不利影响。本研究介绍了一种基于物理的生成式 ROM,它适用于具有参数依赖性的非线性系统,此外还能量化与各自估计值相关的置信度。这项工作的一个主要贡献是将这些参数 ROM 条件化为可从监测测量中获得的特征,并以可行的在线方式进行。这与大多数现有的 ROM 方案相反,这些方案仍然局限于对基于物理的、通常是先验未知的系统参数进行规定。我们的工作利用条件变异自动编码器将所需的还原基础持续映射到从有限的输出测量中提取的特征向量上,同时还允许对 ROM 估算的相关量进行概率评估。为了重新建立与物理模型参数的联系,我们引入了一项辅助任务,即使用基于神经网络的适当概率分布参数化。我们在一系列模拟案例研究中验证了所提出的方案,其中包括与系统属性和输入负载特征相关的参数依赖性下的几何和材料非线性效应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Reduced Order Model conditioned on monitoring features for estimation and uncertainty quantification in engineered systems
Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to quantify the confidence associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.
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