论文标题
用于耦合完整和降低订单模型的多重计算
Multifidelity Computing for Coupling Full and Reduced Order Models
论文作者
论文摘要
混合物理机器学习模型越来越多地用于运输过程的模拟中。许多与科学和工程应用相关的复杂多物理系统包括多个时空量表，并构成了多重级问题，在各种配方或异质计算实体之间共享界面。为此，我们提出了一种强大的混合分析和建模方法，该方法结合了基于物理的完整订单模型（FOM）和数据驱动的减少订单模型（ROM），以形成对预测性数字双胞胎技术的混合保真度描述中综合方法的构建块。在界面上，我们引入了一个长期的短期内存网络，以各种形式的界面误差校正或延长桥接这些高和低保真模型。提出的界面学习方法被测试是解决Rom-Fom耦合问题的一种新方法，以解决非线性对流 - 扩散流动情况，并使用双期性设置来捕获广泛的运输过程的本质。
Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a multifidelity problem sharing an interface between various formulations or heterogeneous computational entities. To this end, we present a robust hybrid analysis and modeling approach combining a physics-based full order model (FOM) and a data-driven reduced order model (ROM) to form the building blocks of an integrated approach among mixed fidelity descriptions toward predictive digital twin technologies. At the interface, we introduce a long short-term memory network to bridge these high and low-fidelity models in various forms of interfacial error correction or prolongation. The proposed interface learning approaches are tested as a new way to address ROM-FOM coupling problems solving nonlinear advection-diffusion flow situations with a bifidelity setup that captures the essence of a broad class of transport processes.
