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New framework optimizes digital twin calibration with budgeted data acquisition

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for calibrating digital twins, which are virtual replicas of physical systems. This framework addresses the challenge of expensive data collection by optimizing the generation of interaction data and strategically using a limited budget for privileged parameter measurements. The approach combines a reinforcement learning controller with a recurrent parameter estimator and a budgeted query policy. Case studies on Pendulum and Waterworld simulations demonstrated the effectiveness of this method in reducing error compared to uncalibrated twins, particularly in data-scarce scenarios. AI

IMPACT This research could lead to more efficient and accurate digital twin calibration, reducing the cost and time required for data collection in complex systems.

RANK_REASON The cluster contains a single academic paper detailing a new methodology for digital twin calibration. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework optimizes digital twin calibration with budgeted data acquisition

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The cluster contains a single academic paper detailing a new methodology for digital twin calibration. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Vladyslava Spitkovska, Dmytro Kuzmenko ·

    Trajectory Design and Budgeted Querying for Digital Twin Calibration

    arXiv:2608.08631v1 Announce Type: new Abstract: Digital-twin calibration requires interaction data that is expensive to collect. We study two acquisition decisions: which trajectories to generate, and when to spend a limited budget on privileged parameter measurements. Our framew…

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