Modeling a Thermionic Electron Source Using a Physics-Informed Neural Network

Abstract

We explore the application of Physics-Informed Neural Networks (PINNs) for simulation of thermionic electron sources. This is motivated by the need for quick surrogate models used to simulate such sources within a digital twin of a complete particle accelerator. Here, a PINN was developed on a simplified model of the thermionic source: the planar diode. This model very accurately simulated the system, and performed significantly better than a traditional neural network while also using less training data. We hope to apply this proof-of-concept in motivating the development of a PINN model for a full thermionic electron source at the University of Chicago.