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QP Based Constrained Optimization for Reliable PINN Training
Alan Williams, Christopher Leon, Alexander Scheinker
Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for integrating physics-based constraints and data to address forward and inverse problems in machine learn…
Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase space
Mahindra Rautela, Alan Williams, Alexander Scheinker
Complex dynamical systems, such as particle accelerators, require tuning over high-dimensional, nonlinear state and parameter spaces while experimental measurements and high-fideli…
Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal
Mahindra Rautela, Alan Williams, Alexander Scheinker
Charged particle dynamics under the influence of electromagnetic fields is a challenging spatiotemporal problem. Many high performance physics-based simulators for predicting behav…
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 (…
Accelerator system parameter estimation using variational autoencoded latent regression
Mahindra Rautela, Alan Williams, Alexander Scheinker
Particle accelerators are time-varying systems whose components are perturbed by external disturbances. Tuning accelerators can be a time-consuming process involving manual adjustm…
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…
A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators
Mahindra Rautela, Alan Williams, Alexander Scheinker
Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to lim…