San Omer, Pawar Suraj, Rasheed Adil
School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK, 74078, USA.
Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN, 37996, USA.
Sci Rep. 2023 Nov 16;13(1):20087. doi: 10.1038/s41598-023-47078-9.
In this article, we introduce a decentralized digital twin (DDT) modeling framework and its potential applications in computational science and engineering. The DDT methodology is based on the idea of federated learning, a subfield of machine learning that promotes knowledge exchange without disclosing actual data. Clients can learn an aggregated model cooperatively using this method while maintaining complete client-specific training data. We use a variety of dynamical systems, which are frequently used as prototypes for simulating complex transport processes in spatiotemporal systems, to show the viability of the DDT framework. Our findings suggest that constructing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems may be made possible by federated machine learning.
在本文中，我们介绍了一种去中心化数字孪生（DDT）建模框架及其在计算科学与工程中的潜在应用。DDT方法基于联邦学习的理念，联邦学习是机器学习的一个子领域，它促进知识交换而不泄露实际数据。客户端可以使用这种方法协同学习一个聚合模型，同时保留完整的特定于客户端的训练数据。我们使用各种动力学系统，这些系统经常被用作模拟时空系统中复杂传输过程的原型，以展示DDT框架的可行性。我们的研究结果表明，通过联邦机器学习，在复杂的非线性时空系统中构建高精度的去中心化数字孪生可能成为现实。
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