Colloquium: Machine learning in nuclear physics
A Boehnlein, M Diefenthaler, N Sato, M Schram… - Reviews of modern …, 2022 - APS
Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …
scientific research. These techniques are being applied across the diversity of nuclear …
New method of traffic flow forecasting based on quantum particle swarm optimization strategy for intelligent transportation system
D Zhang, J Wang, H Fan, T Zhang… - International Journal …, 2021 - Wiley Online Library
Traffic flow forecasting is one of the essential means to realize smart cities and smart
transportation. The accurate and effective prediction will provide an important basis for …
transportation. The accurate and effective prediction will provide an important basis for …
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 …
[PDF][PDF] Artificial intelligence and machine learning in nuclear physics
A Boehnlein, M Diefenthaler, C Fanelli… - arXiv preprint arXiv …, 2021 - academia.edu
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 …
(AI) and machine learning (ML) in nuclear science, covering topics in nuclear theory …
Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics
A Scheinker - arXiv preprint arXiv:2501.04305, 2025 - arxiv.org
Adaptive physics-informed super-resolution diffusion is developed for non-invasive virtual
diagnostics of the 6D phase space density of charged particle beams. An adaptive …
diagnostics of the 6D phase space density of charged particle beams. An adaptive …
New method of traffic flow forecasting based on QPSO strategy for Internet of Vehicles
D Zhang, J Du, T Zhang, H Fan - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
We propose a new method of traffic flow forecasting based on quantum particle swarm
optimization strategy (QPSO) for Internet of Vehicles (IOV). Establish a corresponding model …
optimization strategy (QPSO) for Internet of Vehicles (IOV). Establish a corresponding model …
Machine learning and deep learning models for traffic flow prediction: A survey
A Gobezie, MS Fufa - 2020 - researchsquare.com
Traffic congestion is one of the problems for cities around the world due to the rapid
increasing of vehicles in urbanization. Traffic flow prediction is of a great importance for …
increasing of vehicles in urbanization. Traffic flow prediction is of a great importance for …
Network-wide Traffic Feature Learning and Forecasting Under Non-stationary Circumstances Using Advanced Deep Neural Network
MJ Tsai - 2023 - search.proquest.com
The rapid advancement of intelligent traffic sensing and communication technologies has
introduced a new era of transportation data, offering unprecedented opportunities to predict …
introduced a new era of transportation data, offering unprecedented opportunities to predict …