Conveners
Surrogate Modelling and Digital Twins
- Francesco Maria Velotti (CERN)
Surrogate Modelling and Digital Twins
- Jiao Yi (IHEP)
We describe research in deep learning on graph representations of the injector beamline at the Continuous Electron Beam Accelerator Facility (CEBAF) to develop a tool for operations. We leverage operational archived data – both unlabeled and labeled configurations – to train a graph neural network (GNN) via our methods of self-supervised training and supervised fine tuning. We demonstrate the...
This work presents a machine learning-based approach for compensating magnetic hysteresis in the main dipole and quadrupole magnets of the multi-cycling CERN SPS, utilizing time series neural architectures like the Temporal Fusion Transformers trained on magnetic field measurements. The predicted magnetic fields enable feed-forward, cycle-by-cycle, compensation through the CERN accelerator...
Fast simulations of intense relativistic electron beams can be sufficiently accurate to allow for tuning of an accelerator’s magnetic transport field, but are incapable of capturing all relevant beam physics due to limitations in the model. Because methods that do capture these effects are significantly more computationally-expensive, e.g. particle in-cell simulations, they are fundamentally...
A major challenge in constructing future nuclear fusion power plants is understanding how reactor materials are damaged by the neutron flux generated during the fusion process. In order to address this challenge, the IFMIF-DONES neutron source is being built for material irradiation, generating the necessary neutron flux through a stripping reaction between accelerated deuterons and a lithium...
As the design complexity of modern accelerators grows, there is more interest in using controllable-fidelity simulations that have fast execution time or yield additional insights as compared to standard codes. One notable example of additional information are gradients of physical observables with respect to design parameters produced by differentiable simulations. The IOTA/FAST facility has...
A multifaceted virtual accelerator model that seamlessly integrates with the online experimental system would highly benefit the operators to test and evaluate beam tuning scenarios and apply them online. As part of this effort, the beam dynamics code TRACK is wrapped with control system architecture and the graphic user interface BADGER developed by SLAC. Customizability and task...
Precision control of electron beams is one of the main charges of beam physics, as producing high-brightness beams is critical to numerous accelerator deliverables, including high-quality x-rays from XFELs and high-quality ultrafast probes for UED/UEM. Critical to this effort is a set of accurate system models that can inform control policies. To be useful, these models must accurately...
Beam imaging presents significant challenges due to the necessity of positioning imaging devices near the beam pipe, an area subjected to high levels of radiation that can damage cameras and their peripheral electronics, reducing their lifespan and reliability. With the global discontinuation of radiation-hardened tube cameras previously used for this purpose, a robust and durable replacement...
This study explores various neural network approaches to simulate beam dynamics, specifically addressing non-linear space charge effects. We introduce a convolutional encoder-decoder architecture with skip connections, achieving a relative error of 0.5% in predicting both transversal and coupled 3D electric self-fields. Additionally, to enhance interpretability and robustness, we investigate...
