![\"Predicting](\"https:\/\/www.rochester.edu\/coe\/wp-content\/uploads\/2025\/05\/Predicting-Laser-System-Performance-with-Generative-AI-1024x576.jpg\")
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Team Members: Anjaly George, Jaimin Shah, Karthik Dinesh, Rishabh Sharma<\/em><\/p>\n The goal of the University of Rochester’s\u00a0Laboratory for Laser Energetics (LLE<\/em>) project was to predict laser system performance by modeling the relationship between input and output energy distributions, enabling more precise and efficient experiment designs. To achieve this goal, students developed a two-stage framework, incorporating a ViT-3D Convolver for initial predictions, independent denoisers \u2013 UNet-ResNet for spatial beams, and SFFN-AE for pulse shapes to refine outputs. Additionally, the team explored alternative architectures such as LSTM for pulse-only prediction tasks. The ViT-3D Pulse Denoiser achieved the best results, with a Mean Squared Error (MSE) of 0.947 and an energy difference of 3.66 Joules for pulse predictions. For spatial beam outputs, the UNet-ResNet-based image denoiser demonstrated a low energy difference of 3.35 Joules and a Mean Absolute Percentage Error (MAPE) of 17.8%, showcasing its robustness across both modalities. These findings demonstrate the potential of generative AI for real-time, high-accuracy laser system predictions.<\/p>\n","protected":false},"excerpt":{"rendered":" Team Members: Anjaly George, Jaimin Shah, Karthik Dinesh, Rishabh Sharma The goal of the University of Rochester’s\u00a0Laboratory for Laser Energetics (LLE) project was to predict laser system performance by modeling…<\/p>\n","protected":false},"author":162,"featured_media":16452,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[42],"tags":[152,212],"class_list":["post-16442","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fall-2024-projects","tag-scientific-research","tag-ur-lle"],"yoast_head":"\n