New framework optimizes digital twin calibration with budgeted data acquisition

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 →

• arXiv
• gated recurrent unit
• Pendulum
• random forest
• Waterworld

• paper
• infra

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