用物理约束的扩散模型，从低分辨率图像重建带电粒子束的高精度六维相空间分布。
Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics
- 通过自适应变分自编码器将初始束流数据映射到低维隐空间，生成六维相空间张量。
- 利用物理引导的超分辨率扩散模型，将6D密度图从326像素提升至256×256像素高分辨率。
- 无需已知随时间变化的初始条件，可无监督追踪动态束流，适用于复杂高维系统。
提出一种自适应物理信息超分辨率扩散方法，用于带电粒子束六维相空间密度的非侵入式虚拟诊断。通过自适应变分自编码器（VAE）将初始束流图像与标量测量值嵌入低维隐空间，生成326像素的六维相空间密度张量表示。从该六维张量投影生成物理一致的二维投影。基于物理引导的超分辨率扩散模型，将六维密度的低分辨率图像转化为256×256像素的高分辨率图像。无监督自适应隐空间调优使系统能够在未知随时间变化的初始条件下，实现对时变束流的持续追踪。该方法在HiRES UED实验数据和多粒子模拟中得到验证，且对分布偏移具有鲁棒性，无需重新训练，适用于广泛复杂的高维相空间演化系统。
原文摘要 · Abstract (English)
Adaptive physics-informed super-resolution diffusion is developed for non-invasive virtual diagnostics of the 6D phase space density of charged particle beams. An adaptive variational autoencoder (VAE) embeds initial beam condition images and scalar measurements to a low-dimensional latent space from which a 326 pixel 6D tensor representation of the beam's 6D phase space density is generated. Projecting from a 6D tensor generates physically consistent 2D projections. Physics-guided super-resolution diffusion transforms low-resolution images of the 6D density to high resolution 256x256 pixel images. Un-supervised adaptive latent space tuning enables tracking of time-varying beams without knowledge of time-varying initial conditions. The method is demonstrated with experimental data and multi-particle simulations at the HiRES UED. The general approach is applicable to a wide range of complex dynamic systems evolving in high-dimensional phase space. The method is shown to be robust to distribution shift without re-training.
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