Decentralized digital twins of complex dynamical systems

• URL: http://arxiv.org/abs/2207.12245v1
• Date: Thu, 7 Jul 2022 19:44:42 GMT
• Title: Decentralized digital twins of complex dynamical systems
• Authors: Omer San, Suraj Pawar, Adil Rasheed
• Abstract summary: We introduce a decentralized twin (DDT) framework for dynamical systems. We discuss the prospects of the DDT paradigm in computational science and engineering applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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