Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …

Particle accelerators are extremely complex machines that are challenging to simulate,
design, and control. Over the past decade, artificial intelligence (AI) and machine learning …

Highly polarized, multi-kiloampere-current electron bunches from compact laser-plasma
accelerators are desired for numerous applications. Current proposals to produce these …

libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of
the DOE Exascale Computing Project. The toolkit utilizes a unique generator–simulator …

Numerical modeling of electromagnetic waves is an important tool for understanding the
interaction of light and matter, and lies at the core of computational electromagnetics …

The complex non-linear processes in multi-dimensional parameter spaces, that are typical
for an accelerator, are a natural application for machine learning algorithms. This paper …

Bayesian optimization has emerged as a highly effective tool for the safe online optimization
of systems, due to its high sample efficiency and noise robustness. To further enhance its …

Almost all science and engineering applications eventually stop scaling: their runtime no
longer decreases as available computational resources increase. Therefore, many …

In this paper, we investigate the usage of advanced algorithms, specifically Bayesian
optimization, adapted for optimizing the design and operation of different linear accelerators …

We employ Bayesian optimization combined with three-dimensional particle-in-cell
simulations to identify the optimal laser and plasma parameters that, for a given laser pulse …