Model order reduction (MOR) has become one of the most widely used tools to create efficient surrogate models for time-critical applications. For nonlinear models, however, linear MOR approaches are only practicable to a limited extent. Nonlinear approaches, on the contrary, often require intrusive manipulations of the used simulation code. Hence, nonintrusive MOR approaches using classic model order reduction along with machine learning (ML) algorithms can provide remedy. Such approaches have drawn a lot of attention in the recent years. They rely on the idea to learn the dynamics not in a high dimensional but in a reduced space, that is, they predict the discrete sequence of reduced basis' coefficients. Open questions are the suitability of such methods in the field of structural dynamics and the best choice of the used ML algorithm. Both are addressed in this article in addition to the integration of the methodology into a modular and flexible framework that can effortless be adapted to various requirements. By applying the methodology to a dynamic mechanical system, accurate surrogate models are received, which can speed up the simulation time significantly, while still providing high-quality state approximations.

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一种基于机器学习的结构动力问题非侵入式非线性模型约简方法

模型降阶 (MOR) 已成为为时间关键型应用程序创建高效代理模型的最广泛使用的工具之一。然而，对于非线性模型，线性 MOR 方法仅在有限的范围内可行。相反，非线性方法通常需要对使用的仿真代码进行侵入性操作。因此，使用经典模型降阶和机器学习 (ML) 算法的非侵入式 MOR 方法可以提供补救措施。近年来，这种方法引起了很多关注。他们依靠这个想法来学习不是在高维而是在缩减空间中的动力学，也就是说，他们预测缩减基系数的离散序列。悬而未决的问题是此类方法在结构动力学领域的适用性以及所用 ML 算法的最佳选择。除了将该方法集成到一个模块化和灵活的框架中，可以毫不费力地适应各种需求之外，本文还讨论了这两种情况。通过将该方法应用于动态机械系统，可以接收准确的替代模型，这可以显着加快仿真时间，同时仍然提供高质量的状态近似值。