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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.
arXiv:2607.01356v2 Announce Type: replace-cross Abstract: Cyber-physical systems (CPSs) can be compromised through memory corruption vulnerabilities, which can result in safety violations.
Residential heating accounts for a large share of building energy use, and predictive control strategies can reduce it by anticipating rather than reacting to the room temperature alone.
Manipulating objects requires understanding not only their motion, but also the physical properties that determine it. For articulated objects, these include inertia, friction, and mechanisms such as springs or door closers, whose effects can vary with configuration and velocity.
