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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.
A concept for a digital twin of a mouse is proposed as an embodied dynamical system unifying neural activity, biomechanics, sensory feedback, and environmental interactions. Construction relies on CT, anatomical sections, and Scx-GFP imaging, along with Bonhoeffer-van der Pol neural dynamics.
A prototype system has been developed where EPICS controls beam optics simulations for the CEBAF injector, with the simulation running locally or remotely. The system models a beamline with dozens of magnets and diagnostic devices.
Investigates how many training pilots are needed when a digital twin of a wireless channel is used for channel estimation. The twin is treated as a complementary measurement fused with pilot observations. For the first time, this trade-off is formalized and quantified.
