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Bayesian Calibration of Accelerator Digital Twin with ML
US & West ·
Bayesian methods were used to calibrate the digital twin of a particle accelerator at the AGS Booster at Brookhaven National Laboratory. Multiplicative parameters were introduced to quadrupole transfer functions to account for hard-to-measure effects. A machine learning emulator trained on an ensemble of Bmad simulations accelerated the inference.
Why it matters
Calibrating models of physical systems against measurement data is key for building trustworthy digital twins.
Digital twin simulation models are evolved and redeployed like software, yet DEVS-based engines offer a sound formal basis with little support for versioning, automated validation, or continuous delivery in cloud-native environments, leaving model lifecycle management ad hoc in most deployments.
arXiv:2512.13229v2 Announce Type: replace Abstract: As cyber-physical systems (CPSs) become more dependent on data and communication networks, their vulnerability to false data injection (FDI) attacks has raised significant concerns.
Компания «Актив» завершила работу над итоговым документом по результатам форсайт-сессии «Будущее безопасности киберфизических систем в России. Перспективы и векторы развития», которая прошла в Москве в марте 2025 года.
