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Particle accelerators support a wide array of scientific, industrial, andmedical applications. To meet the needs of these applications, acceleratorphysicists rely heavily on detailed simulations of the complicated particlebeam dynamics through the accelerator. One of the most computationallyexpensive and difficult-to-model effects is the impact of Coherent SynchrotronRadiation (CSR). As a beam travels through a curved trajectory (e.g. due to abending magnet), it emits radiation that in turn interacts with the rest of thebeam. At each step through the trajectory, the electromagnetic field introducedby CSR (called the CSR wakefield) needs to computed and used when calculatingthe updates to the positions and momenta of every particle in the beam. CSR isone of the major drivers of growth in the beam emittance, which is a key metricof beam quality that is critical in many applications. The CSR wakefield isvery computationally intensive to compute with traditional electromagneticsolvers, and this is a major limitation in accurately simulating accelerators.Here, we demonstrate a new approach for the CSR wakefield computation using aneural network solver structured in a way that is readily generalizable to newsetups. We validate its performance by adding it to a standard beam trackingtest problem and show a ten-fold speedup along with high accuracy.

Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-based Charged Particle Beams