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ML-assisted Bayesian Calibration of Accelerator Digital Twin

The study parametrizes hard-to-measure effects and calibrates the Bmad accelerator digital twin using Bayesian methods with beam measurements at AGS Booster, BNL. Computations are accelerated with an ML emulator.

Why it matters

Illustrates approaches to calibrating digital twins of complex technical systems, important for accurate modeling and control.

Relevant to DT products

Original headline
Machine learning assisted Bayesian calibration of an accelerator digital twin from orbit response data
Read the original: arXiv API — полнотекстовый поиск "digital twin"

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