PDE-AI Workshop: ACC 2026

2026-05-26 · 4:15 PM

Abstract

Diffusion-based generative models are the state of the art for representing complex high-dimensional objects, from 3D protein structures to megapixel images to 6D phase-space densities of charged particle beams. For beams governed by the Vlasov-Maxwell equations, the full 6D phase-space distribution evolves under self-consistent nonlinear dynamics, while in practice only limited low-dimensional measurements — often a single 2D projection — are available. This talk explores how generative diffusion models can be combined with adaptive feedback to reconstruct and track such time-varying distributions from partial observations.

Biography

Alexander Scheinker is a Staff Researcher with Los Alamos National Laboratory, where he is the Adaptive Machine Learning Team Leader in the Applied Electrodynamics group. His research focuses on applying advanced control theory and machine learning methods to electrodynamics and for the stabilization and optimization of noisy and analytically unknown complex time-varying systems. Alexander received undergraduate degrees in mathematics and physics from Washington University in St. Louis, in 2006, and the M.S. degree in mathematics and the Ph.D. degree in control theory from the University of California, San Diego, in 2008 and 2012, respectively. He has been developing extremum-seeking (ES) algorithms, proving their stability properties, and applying them for noninvasive diagnostics and for the control of intensely charged particle beams in large particle accelerator facilities. His recent research focuses on combining ES with deep learning methods such as 3-D convolutional neural networks, to develop AI tools that are robust for time-varying systems with distribution shift. He has coauthored more than 20 journal papers on ES theory and applications and the book titled Model-Free Stabilization by Extremum Seeking.