发表机构
University Politehnica Timisoara（蒂米什瓦拉理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出结合随机动态模式分解与深度学习构建高保真数字孪生数据模型，以非侵入方式降复杂度再现流体动力学激波现象，兼顾精度与效率。
AI 中文摘要
本文旨在通过非侵入式技术（即不需要将控制方程伽辽金投影到降阶模态基上）从数值代码输出中识别高保真数字孪生数据模型。作者将数字孪生数据模型（DTM）定义为一种降复杂度模型，其主要特征是镜像原始过程的行为。DTM 的显著优势在于，对于因动力学随时间演化复杂而难以探索的设置，能够以高精度和较低的 CPU 时间及硬件成本再现动力学行为。本文提出了一种新框架，通过结合两种最先进的工具——随机动态模式分解和深度学习人工智能——来创建高效的数字孪生数据模型。结果表明，输出与原始源数据一致，且具有降低复杂度的优势。这些 DTM 在三个复杂度递增的激波现象的数值模拟中进行了研究。作者从数值精度和计算效率两方面对新数字孪生数据模型的性能进行了全面评估。
英文摘要
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.
Journal refModelling 2022, 3(3), 314-332
