Open Access
| Issue |
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
|
|
|---|---|---|
| Article Number | 03009 | |
| Number of page(s) | 6 | |
| Section | Computing Methods and Computer Application | |
| DOI | https://doi.org/10.1051/matecconf/202235503009 | |
| Published online | 12 January 2022 | |
- S.M. Zhang, V. Callaghan. Real-time human posture recognition using an adaptive hybrid classifier[J]. International Journal of Machine Learning and Cybernetics, 2021, 12(2):489-499. [CrossRef] [Google Scholar]
- R. L. Hughes, A continuum theory for the flow of pedestrians[J]. Transp. Res. Part B Methodological, 2002(36): 507–535. [CrossRef] [Google Scholar]
- G. B. Whitham. Linear and Nonlinear Waves[M]. John Wiley, New York, 1974. [Google Scholar]
- A. AW and M.1 Rascle. Resurrection of “Second Order” Models of Traffic Flow[J]. Siam Journal on Applied Mathematics, 2000, 60(3): 916-938. [CrossRef] [Google Scholar]
- G. K. W. Kenway, C. A. Mader, P. He, et al. Effective Adjoint Approaches for Computational Fluid Dynamics[J]. Progress in Aerospace Sciences, 2019, 110. [Google Scholar]
- D. Helbing. A Fluid Dynamic Model for me Movement of Pedestrians[J], Complex Systems, 1998, 6(5): 391-415. [Google Scholar]
- T. Murata, K. Fukami, K. Fukagata. Nonlinear mode decomposition with convolutional neural networks for fluid dynamics[J]. JOURNAL OF FLUID MECHANICS, 2020,882. [Google Scholar]
- R. Lubas, J. Miller, M. Mycek, J. Porzycki and J. Was. Three Different Approaches in Pedestrian Dynamics Modeling: A Case Study[J]. NEW RESULTS IN DEPENDABILITY AND COMPUTER SYSTEMS, 2013, 224: 285-294. [CrossRef] [Google Scholar]
- C. J. Cotter, D. Crisan, D. D. Holm, et al. Numerically Modeling Stochastic Lie Transport in Fluid Dynamics[J]. SIAM Journal on Multiscale Modeling and Simulation, 2019, 17(1):192-232. [CrossRef] [Google Scholar]
- M.U. Farooq, M.N.B.M. Saad, A.S. Malik, et al. Motion estimation of high density crowd using fluid dynamics[J]. IMAGING SCIENCE JOURNAL, 2020, 68(3):141-155. [CrossRef] [Google Scholar]
- N. J. Kutz, Deep learning in fluid dynamics[J]. Journal of Fluid Mechanics, 2017, 814:1-4. [CrossRef] [Google Scholar]
- P. Romatschke, Relativistic Fluid Dynamics Far From Local Equilibrium[J]. Physical Review Letters, 2018, 120(1):012301. [CrossRef] [Google Scholar]
- Z. Wang, D. Xiao, F. Fang, et al. Model identification of reduced order fluid dynamics systems using deep learning[J]. International Journal for Numerical Methods in Fluids, 2018, 86(4):255-268. [CrossRef] [Google Scholar]
- Zahra Shahhoseini, Majid Sarvi. Pedestrian crowd flows in shared spaces: Investigating the impact of geometry based on micro and macro scale measures[J]. Transportation Research Part B, 2019, 122. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.