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Abstract
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.
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1 Oklahoma State University, School of Mechanical and Aerospace Engineering, Stillwater, USA (GRID:grid.65519.3e) (ISNI:0000 0001 0721 7331); University of Tennessee, Department of Mechanical, Aerospace and Biomedical Engineering, Knoxville, USA (GRID:grid.411461.7) (ISNI:0000 0001 2315 1184)
2 Oklahoma State University, School of Mechanical and Aerospace Engineering, Stillwater, USA (GRID:grid.65519.3e) (ISNI:0000 0001 0721 7331)
3 Norwegian University of Science and Technology, Department of Engineering Cybernetics, Trondheim, Norway (GRID:grid.5947.f) (ISNI:0000 0001 1516 2393); SINTEF Digital, Department of Mathematics and Cybernetics, Trondheim, Norway (GRID:grid.5947.f) (ISNI:0000 0004 7908 7881)