Physics-Enhanced Multiscale Y-Net for Knife-Edge Wavefront Sensing

To address these limitations, this paper proposes Physics-Enhanced Multiscale Y-Net for Knife-Edge Wavefront Sensing. This method directly reconstructs the target wavefront from shadowgraphs, bypassing traditional numerical procedures. It improves both reconstruction accuracy and speed while enhancing detection resolution.

The network takes two image-plane shadowgraphs as input and outputs the object wavefront. Training combines pre-training on simulated data with physics-informed fine-tuning. Simulation results show that the reconstructed wavefront closely matches the ground truth, with PV error within 6% and RMS error within 8%.

Experimental results demonstrate PV error within 5% and RMS error within 8%. The approach demonstrates high accuracy across error metrics for wavefront detection, offering significant potential for efficient fabrication and inspection of high-power laser components.