Abstract
Abstract
Diffusion generative models are promising alternatives for
fast surrogate models, producing high-fidelity physics
simulations. However, the generation time often requires an
expensive denoising process with hundreds of function evaluations,
restricting the current applicability of these models in a realistic
setting. In this work, we report updates on the CaloScore
architecture, detailing the changes in the diffusion process, which
produces higher quality samples, and the use of progressive
distillation, resulting in a diffusion model capable of generating
new samples with a single function evaluation. We demonstrate these
improvements using the Calorimeter Simulation Challenge 2022
dataset.
Subject
Mathematical Physics,Instrumentation