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 …
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 …
[HTML][HTML] Physics-constrained 3D convolutional neural networks for electrodynamics
A Scheinker, R Pokharel - APL Machine Learning, 2023 - pubs.aip.org
We present a physics-constrained neural network (PCNN) approach to solving Maxwell's
equations for the electromagnetic fields of intense relativistic charged particle beams. We …
equations for the electromagnetic fields of intense relativistic charged particle beams. We …
Supervised stochastic Levenberg–Marquardt intelligent netwoks for dynamics of convective Eyring–Powell magneto-nanofluid model
The presented work examines the dynamics of convective Eyring–Powell magneto-
nanofluid model (CEP-MNFM) with a stretching cylinder by using stupendous knacks of …
nanofluid model (CEP-MNFM) with a stretching cylinder by using stupendous knacks of …
Optics measurement and correction strategies for HL-LHC
X Buffat, A Wegscheider, DW Wolf, H Garcia Morales… - 2022 - cds.cern.ch
Optics Measurement and Correction Strategies for HL-LHC Page 1 CERN-ACC-2022-0004
22/01/2024 CERN-ACC-2022-0004 . rogelio.tomas@cern.ch Optics Measurement and …
22/01/2024 CERN-ACC-2022-0004 . rogelio.tomas@cern.ch Optics Measurement and …
Research on tune feedback of the Hefei Light Source II based on machine learning
YB Yu, GF Liu, W Xu, C Li, WM Li, K Xuan - Nuclear Science and …, 2022 - Springer
The theory of tune feedback correction and the principle of a feedback algorithm based on
machine learning are introduced, with a focus on the application of lasso regression for tune …
machine learning are introduced, with a focus on the application of lasso regression for tune …
[HTML][HTML] Supervised Stochastic Approach for computational analysis of convectively heated magnetized nanofluid flow with bioconvection aspects
Our study delves into the dynamics of a convective Magneto-Hydrodynamic Bioconvective
Nanofluid model (MHD-BCNFM) flowing over a convectively heated stretched sheet. To …
Nanofluid model (MHD-BCNFM) flowing over a convectively heated stretched sheet. To …
6D Phase space diagnostics based on adaptively tuned physics-informed generative convolutional neural networks
A physics-informed generative convolutional neural network (CNN)-based 6D phase space
diagnostic is presented which generates all 15 unique 2D projections (x, y),(x, y'),...,(z, E) of …
diagnostic is presented which generates all 15 unique 2D projections (x, y),(x, y'),...,(z, E) of …