Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks
Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. However, finding optimised operation points for these complex machines is a challenging task due to the large number of parameters involved and the underlying non-linear dynamics. Here, we intro...
Main Authors: | Renato Bellotti, Romana Boiger, Andreas Adelmann |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Information |
Subjects: | |
Online Access: | https://www.mdpi.com/2078-2489/12/9/351 |
Similar Items
-
Efficient Layer-Wise <i>N</i>:<i>M</i> Sparse CNN Accelerator with Flexible SPEC: Sparse Processing Element Clusters
by: Xiaoru Xie, et al.
Published: (2023-02-01) -
A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators
by: Sichen Li, et al.
Published: (2021-03-01) -
AoCStream: All-on-Chip CNN Accelerator with Stream-Based Line-Buffer Architecture and Accelerator-Aware Pruning
by: Hyeong-Ju Kang, et al.
Published: (2023-09-01) -
MobileNets Can Be Lossily Compressed: Neural Network Compression for Embedded Accelerators
by: Se-Min Lim, et al.
Published: (2022-03-01) -
Neural networks as effective surrogate models of radio-frequency quadrupole particle accelerator simulations
by: Joshua Villarreal, et al.
Published: (2024-01-01)