论文标题
复杂动力学系统的分散数字双胞胎
Decentralized digital twins of complex dynamical systems
论文作者
论文摘要
在本文中，我们为动态系统介绍了一个分散的数字双（DDT）框架，并讨论了计算科学和工程应用中DDT建模范式的前景。 DDT方法建立在联合学习概念上，该概念是机器学习的一个分支，该分支鼓励知识共享而无需共享实际数据。这种方法使客户能够协作学习一个汇总的模型，同时将所有培训数据保留在每个客户端。我们证明了DDT框架具有各种动力学系统的可行性，这些动力系统通常被认为是为时空扩展系统中的复杂传输现象建模的原型。我们的结果表明，联合机器学习可能是在复杂的非线性时空系统中设计高度准确的分散数字双胞胎的关键推动力。
In this paper, we introduce a decentralized digital twin (DDT) framework for dynamical systems and discuss the prospects of the DDT modeling paradigm in computational science and engineering applications. The DDT approach is built on a federated learning concept, a branch of machine learning that encourages knowledge sharing without sharing the actual data. This approach enables clients to collaboratively learn an aggregated model while keeping all the training data on each client. We demonstrate the feasibility of the DDT framework with various dynamical systems, which are often considered prototypes for modeling complex transport phenomena in spatiotemporally extended systems. Our results indicate that federated machine learning might be a key enabler for designing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems.
