2022 review of data-driven plasma science
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 …
review article highlights the latest development and progress in the interdisciplinary field of …
Machine learning for design and control of particle accelerators: A look backward and forward
Particle accelerators are extremely complex machines that are challenging to simulate,
design, and control. Over the past decade, artificial intelligence (AI) and machine learning …
design, and control. Over the past decade, artificial intelligence (AI) and machine learning …
Rediscovering orbital mechanics with machine learning
We present an approach for using machine learning to automatically discover the governing
equations and unknown properties (in this case, masses) of real physical systems from …
equations and unknown properties (in this case, masses) of real physical systems from …
Bridging the gap between qualitative and quantitative assessment in science education research with machine learning—A case for pretrained language models …
Science education researchers typically face a trade-off between more quantitatively
oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of …
oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of …
Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster
J St. John, C Herwig, D Kafkes, J Mitrevski… - … Review Accelerators and …, 2021 - APS
We describe a method for precisely regulating the gradient magnet power supply (GMPS) at
the Fermilab Booster accelerator complex using a neural network trained via reinforcement …
the Fermilab Booster accelerator complex using a neural network trained via reinforcement …
Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying …
We present a general adaptive latent space tuning approach for improving the robustness of
machine learning tools with respect to time variation and distribution shift. We demonstrate …
machine learning tools with respect to time variation and distribution shift. We demonstrate …
An adaptive approach to machine learning for compact particle accelerators
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 …
outputs of large complex systems directly from data. However, for time-varying systems, the …
Adaptive machine learning for time-varying systems: low dimensional latent space tuning
A Scheinker - Journal of Instrumentation, 2021 - iopscience.iop.org
Abstract Machine learning (ML) tools such as encoder-decoder convolutional neural
networks (CNN) can represent incredibly complex nonlinear functions which map between …
networks (CNN) can represent incredibly complex nonlinear functions which map between …
Transverse phase space tomography in an accelerator test facility using image compression and machine learning
A Wolski, MA Johnson, M King, BL Militsyn… - … Review Accelerators and …, 2022 - APS
We describe a novel technique, based on image compression and machine learning, for
transverse phase space tomography in two degrees of freedom in an accelerator beamline …
transverse phase space tomography in two degrees of freedom in an accelerator beamline …
Supervised learning-based reconstruction of magnet errors in circular accelerators
Magnetic field errors and misalignments cause optics perturbations, which can lead to
machine safety issues and performance degradation. The correlation between magnetic …
machine safety issues and performance degradation. The correlation between magnetic …