Decentralized digital twins of complex dynamical systems
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 80 references that are in the map. First posted on arXiv 7 Jul 2022.
Builds on
26 of 80 references.
References · 26
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14 more
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- 2019Digital Twin: Values, Challenges and Enablerscited by 81
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.