23–28 Aug 2026
America/Los_Angeles timezone

Differentiable 1D FEL Simulation for Gradient-Based Optimization and Inference

MOP16
24 Aug 2026, 16:00
2h
Poster Presentation Session 3: FEL theory and Machine Learning Monday Poster Session

Speaker

Jingyi Tang (Stanford University)

Description

We present a differentiable 1D free-electron laser (FEL) simulation framework based on automatic differentiation. In contrast to conventional FEL codes that provide only forward simulation, the differentiable formulation enables efficient evaluation of gradients of FEL observables with respect to beam, undulator, and model parameters. This makes it possible to apply gradient-based methods to optimization, sensitivity analysis, parameter fitting, and inverse problems within a physics-based FEL mode

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Authors

Jingyi Tang (Stanford University) Zhirong Huang (SLAC National Accelerator Laboratory)

Presentation materials

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