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Lightning Talk: Differentiable In-Situ Diagnostics for Particle Accelerator Digital Twins
DescriptionSimulation codes are extensively used to design and operate particle accelerators.
Significant effort is invested towards digital twins that closely mirror a physical system and inform tuning of particle accelerators in real time, during operation. Some aspects of these digital twins benefit from using differentiable simulation codes that can automatically compute the gradient of their output with respect to certain input parameters. Gradients can also be used to find the optimal operating point of an accelerator (e.g. maximizing beam energy) and thereby inform tuning of the physical accelerator.
This talk will point out the crucial need for in-situ diagnostic in this type of gradient-based workflow. It is crucial that modeling reproduces the accelerator diagnostics in-situ rather than in post-processing, so that the gradients can propagate throughout the simulation pipeline. We demonstrate a real-world accelerator beamline model from PyTorch and will describe how to incorporate differentiable diagnostics in a C++ code.
Significant effort is invested towards digital twins that closely mirror a physical system and inform tuning of particle accelerators in real time, during operation. Some aspects of these digital twins benefit from using differentiable simulation codes that can automatically compute the gradient of their output with respect to certain input parameters. Gradients can also be used to find the optimal operating point of an accelerator (e.g. maximizing beam energy) and thereby inform tuning of the physical accelerator.
This talk will point out the crucial need for in-situ diagnostic in this type of gradient-based workflow. It is crucial that modeling reproduces the accelerator diagnostics in-situ rather than in post-processing, so that the gradients can propagate throughout the simulation pipeline. We demonstrate a real-world accelerator beamline model from PyTorch and will describe how to incorporate differentiable diagnostics in a C++ code.
Author/Presenters
Event Type
Workshop
TimeSunday, 16 November 202511:45am - 11:55am CST
Location242
AI, Machine Learning, & Deep Learning
Clouds & Distributed Computing
Performance Evaluation, Scalability, & Portability
Scientific & Information Visualization