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Related Experiment Video

Updated: Jul 11, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

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Published on: February 14, 2025

Decentralized digital twins of complex dynamical systems.

Omer San^1,2, Suraj Pawar³, Adil Rasheed^4,5

¹School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK, 74078, USA. osan@utk.edu.

Scientific Reports

|November 17, 2023

Summary

This study presents a decentralized digital twin (DDT) framework using federated learning for collaborative model building without sharing raw data. This approach enables accurate DDTs for complex systems, advancing computational science.

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Area of Science:

Computational Science and Engineering
Machine Learning
Digital Twins

Background:

Digital twins are crucial for simulating complex systems.
Existing methods often require centralized data, posing privacy and logistical challenges.
Federated learning offers a privacy-preserving alternative for distributed model training.

Purpose of the Study:

Introduce a novel decentralized digital twin (DDT) modeling framework.
Demonstrate the framework's applicability in computational science and engineering.
Showcase the potential of federated learning in creating accurate DDTs.

Main Methods:

Developed a DDT modeling framework based on federated learning principles.
Utilized federated learning to enable cooperative model learning among clients.
Ensured client-specific training data remains private during the learning process.

Main Results:

Successfully demonstrated the viability of the DDT framework using various dynamical systems.
Showcased the framework's effectiveness in simulating complex transport processes in spatiotemporal systems.
Validated the potential for high-accuracy DDT construction in nonlinear spatiotemporal systems.

Conclusions:

Federated learning is a viable method for constructing highly accurate decentralized digital twins.
The proposed DDT framework offers a scalable and privacy-preserving solution for complex system modeling.
This approach has significant potential for applications in computational science and engineering.