基于复合神经网络的多保真度数据融合动态响应变化的有效表征
Efficient Characterization of Dynamic Response Variation Using Multi-Fidelity Data Fusion through Composite Neural Network
摘要：
结构的不稳定性是不可避免的，这通常会导致动力响应预测的变化。对于复杂的结构，由于单次运行可能已经是计算昂贵的，因此用于响应变化分析的蛮力蒙特卡罗模拟是不可行的。因此，已经探索了数据驱动的元建模方法以促进有效的仿真和统计推断。元模型的性能取决于训练数据集的质量和数量。然而，在实际应用中，从高维有限元模拟或实验获得的高保真数据通常是稀缺的，这对元模型的建立提出了重大挑战。在本研究中，我们利用结构动力分析中的多级响应预测机会，即，从降阶建模中快速获取大量低保真数据，从全尺寸有限元分析中准确获取少量高保真数据。具体来说，我们制定了一个复合神经网络融合方法，可以充分利用多层次，异构数据集获得。它隐含地识别的相关性的低和高保真度的数据集，从而产生更高的精度相比，与国家的最先进的。全面的调查，使用频率响应变化特性的情况下的例子进行演示的性能。
英文摘要：
Uncertainties in a structure is inevitable, which generally lead to variation in dynamic response predictions. For a complex structure, brute force Monte Carlo simulation for response variation analysis is infeasible since one single run may already be computationally costly. Data driven meta-modeling approaches have thus been explored to facilitate efficient emulation and statistical inference. The performance of a meta-model hinges upon both the quality and quantity of training dataset. In actual practice, however, high-fidelity data acquired from high-dimensional finite element simulation or experiment are generally scarce, which poses significant challenge to meta-model establishment. In this research, we take advantage of the multi-level response prediction opportunity in structural dynamic analysis, i.e., acquiring rapidly a large amount of low-fidelity data from reduced-order modeling, and acquiring accurately a small amount of high-fidelity data from full-scale finite element analysis. Specifically, we formulate a composite neural network fusion approach that can fully utilize the multi-level, heterogeneous datasets obtained. It implicitly identifies the correlation of the low- and high-fidelity datasets, which yields improved accuracy when compared with the state-of-the-art. Comprehensive investigations using frequency response variation characterization as case example are carried out to demonstrate the performance.
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