Control of the 6D (x,y,z,p x,p y,p z) phase space distribution of beams is important for all particle accelerators over a wide span of applications including beam - based imaging for material science, accelerator - based light sources, and plasma wakefield - based accelerators. At all large high intensity beam particle accelerators, such as the Los Alamos Neutron Science Center (LAN - SCE) [1] or the Spallation Neutron Source (SNS) [2], the beam's phase distribution must be controlled for proper acceleration and to prevent beam lo ss by matching to the accelerator's magnetic focusing lattice. At particle accelerator - based free electron laser (FEL) light sources, such as the Linac Coherent Light Source [3], the European X - ray FEL [4], and the Swiss FEL [5] the beam's phase space defi nes the properties of the generated light and must be adjusted between different experiments.

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.

Working at breakneck speed, a team of engineering and medical professionals at Stanford have created two novel computer tools that can tell local governments and hospitals whether they are about to be overwhelmed by the COVID-19 pandemic. One of the new calculators provides county-by-county predictions of hospitalizations tied to the coronavirus. The other allows individual hospitals to predict their own shortages of intensive care beds, ventilators and staffing. The tools were developed in mere weeks, starting in mid-March, by a group at Stanford Engineering that specializes in solving operational problems for hospitals. The team, called Systems Utilization for Stanford Medicine or SURF Stanford Medicine, is headed by David Scheinker, an adjunct professor at the School of Engineering and a clinical associate professor at the School of Medicine.