Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and
outputs of large complex systems directly from data. However, for time-varying systems, the …

Machine learning (ML) is growing in popularity for various particle accelerator applications
including anomaly detection such as faulty beam position monitor or RF fault identification …

This review represents a summary of recent work in the application of artificial intelligence
(AI) and machine learning (ML) in nuclear science, covering topics in nuclear theory …

Machine learning (ML) tools such as encoder-decoder deep convolutional neural networks
(CNN) are able to extract relationships between inputs and outputs of large complex …

With a Bayesian Gaussian regression approach, a systematic method for analyzing a
storage ring's beam position monitor (BPM) system requirements has been developed. The …

Powerful deep learning tools, such as convolutional neural networks (CNN), are able to
learn the input-output relationships of large complicated systems directly from data. Encoder …

In this paper we expand the technique of characterizing nonlinear optics from off-energy
closed orbits (NOECO) to cover harmonic sextupoles in storage rings. The existing NOECO …

Machine learning (ML) tools are able to learn relationships between the inputs and outputs
of large complex systems directly from data. However, for time-varying systems, the …