Physics-Guided Inverse Design of Optical Waveforms for Nonlinear Electromagnetic Dynamics

TL;DR

This paper introduces a physics-guided deep learning method for inverse designing optical waveforms that optimize nonlinear electromagnetic system performance, demonstrated on electron beam generation with significant emittance reduction.

Contribution

The work presents a novel physics-guided deep learning framework for inverse optical waveform design, enabling efficient compensation of nonlinear distortions in complex photonic systems.

Findings

Achieved over 52% suppression of extrinsic emittance growth in simulations.

Demonstrated 31% reduction in emittance contribution using experimentally synthesized waveforms.

Established a general approach for physics-guided inverse design in nonlinear optical systems.

Abstract

Structured optical waveforms are emerging as powerful control fields for the next generation of complex photonic and electromagnetic systems, where the temporal structure of light can determine the ultimate performance of scientific instruments. However, identifying optimal optical drive fields in strongly nonlinear regimes remains challenging because the mapping between optical inputs and system response is high-dimensional and typically accessible only through computationally expensive simulations. Here, we present a physics-guided deep learning framework for the inverse design of optical temporal waveforms. By training a light-weighted surrogate model on simulations, the method enables gradient-based synthesis of optical profiles that compensate nonlinear field distortions in driven particle-field systems. As a representative application, we apply the approach to the generation of electron beams used in advanced photon and particle sources. The learned optical waveform actively suppresses extrinsic emittance growth by more than 52% compared with conventional Gaussian operation and by approximately 9% relative to the theoretical flattop limit in simulation. We further demonstrate experimental feasibility by synthesizing the predicted waveform using a programmable pulse-shaping platform; incorporating the measured optical profile into beamline simulations yields a 31% reduction in the extrinsic emittance contribution. Beyond accelerator applications, this work establishes a general way for physics-guided inverse design of optical control fields, enabling structured light to approach fundamental performance limits in nonlinear photonic and high-frequency electromagnetic systems.