• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 195 matches

• 2021
• 
  Lucio Santi, Lucas Rossi, Rodrigo D. Castro:
  Efficient discrete-event based particle tracking simulation for high energy physics. Comput. Phys. Commun. 258: 107619 (2021)
• 
  Weihao Song, Zidong Wang, Jianan Wang, Jiayuan Shan:
  Particle filtering for a class of cyber-physical systems under Round-Robin protocol subject to randomly occurring deception attacks. Inf. Sci. 544: 298-307 (2021)
• 
  Matthew Feickert, Benjamin Nachman:
  A Living Review of Machine Learning for Particle Physics. CoRR abs/2102.02770 (2021)
• 2020
• 
  Blerina Gkotse:
  Génération de systèmes de gestion de données personnalisés fondée sur les ontologies: une application à la physique expérimentale des particules. (Ontology-based Generation of Personalised Data Management Systems: an Application to Experimental Particle Physics). Paris Sciences et Lettres University, France, 2020
• 
  Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Anna Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Dylan Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu:
  Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. Frontiers Big Data 3: 598927 (2020)
• 
  Guizhen Mai, Yinghan Hong, Shen Fu, Yingqing Lin, Zhifeng Hao, Han Huang, Yuanhao Zhu:
  Optimization of Lennard-Jones clusters by particle swarm optimization with quasi-physical strategy. Swarm Evol. Comput. 57: 100710 (2020)
• 
  Vito Antonelli, Lino Miramonti, Marco Danilo Claudio Torri:
  Phenomenological Effects of CPT and Lorentz Invariance Violation in Particle and Astroparticle Physics. Symmetry 12(11): 1821 (2020)
• 
  Doreen Fraser:
  The development of renormalization group methods for particle physics: Formal analogies between classical statistical mechanics and quantum field theory. Synth. 197(7): 3027-3063 (2020)
• 
  Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, Christoforos Somarakis, John S. Baras:
  Learning physical laws: the case of micron size particles in dielectric fluid. ACC 2020: 2949-2954
• 
  Alexander Bogatskiy, Brandon M. Anderson, Jan T. Offermann, Marwah Roussi, David W. Miller, Risi Kondor:
  Lorentz Group Equivariant Neural Network for Particle Physics. ICML 2020: 992-1002
• 
  Annesha Mazumder, Syed Azeemuddin, Tapan K. Sau, Prabhakar Bhimalapuram:
  Study of Gold Particles in HFSS with Varying Physical Parameters and Arrangements. NEMS 2020: 529-532
• 
  Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne:
  PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly. SDM 2020: 559-567
• 
  Sioni Summers, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Duc Hoang, Sergo Jindariani, Edward Kreinar, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Dylan Rankin, Nhan Tran, Zhenbin Wu:
  Fast inference of Boosted Decision Trees in FPGAs for particle physics. CoRR abs/2002.02534 (2020)
• 
  Henning Wessels, Christian Weißenfels, Peter Wriggers:
  The Neural Particle Method - An Updated Lagrangian Physics Informed Neural Network for Computational Fluid Dynamics. CoRR abs/2003.10208 (2020)
• 
  Corey Adams, Kazuhiro Terao, Taritree Wongjirad:
  PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics. CoRR abs/2006.01993 (2020)
• 
  Alexander Bogatskiy, Brandon M. Anderson, Jan T. Offermann, Marwah Roussi, David W. Miller, Risi Kondor:
  Lorentz Group Equivariant Neural Network for Particle Physics. CoRR abs/2006.04780 (2020)
• 
  Andrei Ivanov, Ilya Agapov:
  Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators. CoRR abs/2007.03555 (2020)
• 
  Hendrik Weerts:
  Physics Letters B, Physical Review Letters and arXiv publications. Correlating PLB, PRL and arXiv articles for nuclear, particle and astro-physics. CoRR abs/2007.09555 (2020)
• 
  Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Anna Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Dylan Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu:
  Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. CoRR abs/2008.03601 (2020)
• 
  Xiaoli Chen, Liu Yang, Jinqiao Duan, George Em Karniadakis:
  Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks. CoRR abs/2008.10653 (2020)
• 
  Adi Hanuka, Xiaobiao Huang, Jane Shtalenkova, Dylan Kennedy, Auralee Edelen, V. R. Lalchand, Daniel Ratner, Joseph Duris:
  Physics-informed Gaussian Process for Online Optimization of Particle Accelerators. CoRR abs/2009.03566 (2020)
• 
  Dan Koschier, Jan Bender, Barbara Solenthaler, Matthias Teschner:
  Smoothed Particle Hydrodynamics Techniques for the Physics Based Simulation of Fluids and Solids. CoRR abs/2009.06944 (2020)
• 
  Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer:
  Hierarchical clustering in particle physics through reinforcement learning. CoRR abs/2011.08191 (2020)
• 2019
• 
  Jing-Long Chen, Ri-Xin Wang, Yuqing Li, Minqiang Xu:
  A Simplified Extension of Physics-Based Single Particle Model for Dynamic Discharge Current. IEEE Access 7: 186217-186227 (2019)
• 
  Placido Fernandez Declara, Daniel Hugo Cámpora Pérez, Javier García-Blas, Dorothea Vom Bruch, José Daniel García, Niko Neufeld:
  A Parallel-Computing Algorithm for High-Energy Physics Particle Tracking and Decoding Using GPU Architectures. IEEE Access 7: 91612-91626 (2019)
• 
  Zhaoxia Duan, Jahanzeb Akhtar, Imran Ghous, Muhammad Jawad, Ikramullah Khosa, Khurram Ali:
  Improving the Tracking of Subatomic Particles Using the Unscented Kalman Filter With Measurement Redundancy in High Energy Physics Experiments. IEEE Access 7: 61728-61737 (2019)
• 
  Tetsu Kamiya, Yoshio Toyama, Keigo Hanyu, Megumi Takai, Takahiro Kikuchi, Yukihiro Michiwaki, Seiichi Koshizuka:
  Numerical visualisation of physical values during human swallowing using a three-dimensional swallowing simulator 'Swallow Vision®' based on the moving particle simulation method. Comput. methods Biomech. Biomed. Eng. Imaging Vis. 7(4): 382-388 (2019)
• 
  Barbara Demin, Sandra Parlati, Piero Spinnato, Stefano Stalio:
  U-LITE, a Private Cloud Approach for Particle Physics Computing. Int. J. Cloud Appl. Comput. 9(1): 1-15 (2019)
• 
  W. C. Moore, S. Balachandar, Georges Akiki:
  A hybrid point-particle force model that combines physical and data-driven approaches. J. Comput. Phys. 385: 187-208 (2019)
• 
  Torsten Asselmeyer-Maluga:
  Braids, 3-Manifolds, Elementary Particles: Number Theory and Symmetry in Particle Physics. Symmetry 11(10): 1298 (2019)

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