Publications (43)
Towards latent space evolution of spatiotemporal dynamics of six-dimensional phase space of charged particle beams
Mahindra Rautela, Alan Williams, Alexander Scheinker
Addressing the charged particle beam diagnostics in accelerators poses a formidable challenge, demanding high-fidelity simulations in limited computational time. Machine learning (…
Conditional Guided Generative Diffusion for Particle Accelerator Beam Diagnostics
Alexander Scheinker
Advanced accelerator-based light sources such as free electron lasers (FEL) accelerate highly relativistic electron beams to generate incredibly short (10s of femtoseconds) coheren…
Practical Safe Extremum Seeking with Assignable Rate of Attractivity to the Safe Set
Alan Williams, Miroslav Krstic, Alexander Scheinker
We present Assignably Safe Extremum Seeking (ASfES), an algorithm designed to minimize a measured objective function while maintaining a measured metric of safety (a control barrie…
Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading
Mahindra Rautela, Alexander Most, Siddharth Mansingh +9
Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks. Their utility under extreme-loading material dynamics remains unclear. We benchmark out-of-dis…
Experimental Safe Extremum Seeking for Accelerators
Alan Williams, Alexander Scheinker, En-Chuan Huang +2
We demonstrate the recent designs of Safe Extremum Seeking (Safe ES) on the 1 kilometer-long charged particle accelerator at the Los Alamos Neutron Science Center (LANSCE). Safe ES…
Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuning
Alexander Scheinker
Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of image…