一种结合随机动态模态分解和深度学习的高保真数字孪生数据模型
High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics
这篇论文介绍了一种新方法，结合了随机动态模态分解和深度学习来创建更高效的数字孪生数据模型，可能对流体动力学领域的研究者有用。
本文提出了一种新框架，通过结合随机动态模态分解和深度学习来创建高效数字孪生数据模型。该模型从数值代码输出中识别出高保真数据模型，无需侵入性技术。在三个激波现象的数值模拟中，该模型在保持与原始数据一致性的同时，显著降低了复杂性。
High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics
The purpose of this paper is the identification of high-fidelity digital twin data models from numerical code outputs by non-intrusive techniques (i.e., not requiring Galerkin projection of the governing equations onto the reduced modes basis). In this paper the author defines the concept of the digital twin data model (DTM) as a model of reduced complexity that has the main feature of mirroring the original process behavior. The significant advantage of a DTM is to reproduce the dynamics with high accuracy and reduced costs in CPU time and hardware for settings difficult to explore because of the complexity of the dynamics over time. This paper introduces a new framework for creating efficient digital twin data models by combining two state-of-the-art tools: randomized dynamic mode decomposition and deep learning artificial intelligence. It is shown that the outputs are consistent with the original source data with the advantage of reduced complexity. The DTMs are investigated in the numerical simulation of three shock wave phenomena with increasing complexity. The author performs a thorough assessment of the performance of the new digital twin data models in terms of numerical accuracy and computational efficiency.
