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August 4, 2025

Real-time digital twins: unifying bias-aware data assimilation and machine learning

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Overview

Seminar reveals new paradigm in digital twins using data assimilation and machine learning for robust control in complex systems.

Key Points

  • MAIN FINDING: The seminar introduces a digital twin framework that integrates data assimilation and machine learning for improved modeling and control.
  • KEY EVIDENCE: A bias-aware data assimilation framework effectively combines low-order models and real data, addressing modeling errors.
  • APPROACH: Techniques involve model-error inference, handling partial observations, and physical control strategies for real-time applications.
  • SIGNIFICANCE: These advancements enhance stability and scalability in chaotic environments, paving the way for autonomous systems and real-time feedback control.

Cite This Study

A 2025 study studied this question.

synapsesocial.com/papers/689a0f86e6551bb0af8d0b34https://doi.org/10.52843/cassyni.svynpv
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Also Consider

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  5. 5From Mathematical Modeling and Simulation to Digital Twins: Bridging Theory and Digital Realities in Industry and Emerging Technologies2025 · 43 citations