Open Access
| Issue |
EPJ Web Conf.
Volume 358, 2026
EFM25 – Energy & Fluid Mechanics 2025
|
|
|---|---|---|
| Article Number | 01010 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202635801010 | |
| Published online | 12 March 2026 | |
- R. Garcia-Fernandez, K. Portal-Porras, O. Iri-garay, Z. Ansa, U. Fernandez-Gamiz, Cnn-based flow field prediction for bus aerodynamics analysis, Scientific Reports 13, 21213 (2023). https://doi.org/10.1038/s41598-023-48419-4 [Google Scholar]
- S.J. Jacob, M. Mrosek, C. Othmer, H. Köstler, Benchmarking convolutional neural network and graph neural network based surrogate mod-els on a real-world car external aerodynamics dataset, Computers Fluids 300, 106760 (2025). https://doi.org/10.1016/j.compfluid.2025.106760 [Google Scholar]
- T. Chen, R. Li, X. Hu, B. Zhang, Y. Liu, L.L. Wang, N. Gao, Machine learning as cfd surrogate models for rapid prediction of building-related phys-ical fields: A review of methods and state-of-the-art, Building and Environment p. 113667 (2025). https://doi.org/10.1016/j.buildenv.2025.113667 [Google Scholar]
- R. Mao, Y. Lan, L. Liang, T. Yu, M. Mu, W. Leng, Z. Long, Rapid cfd prediction based on machine learning surrogate model in built en-vironment: A review, Fluids 10, 193 (2025). https://doi.org/10.3390/fluids10080193 [Google Scholar]
- M.Y. Wu, Y. Wu, X.Y. Yuan, Z.H. Chen, W.T. Wu, N. Aubry, Fast prediction of flow field around airfoils based on deep convolutional neu-ral network, Applied Sciences 12, 12075 (2022). https://doi.org/10.3390/app122312075 [Google Scholar]
- J. Hu, W. Zhang, Flow field modeling of airfoil based on convolutional neural networks from transform domain perspective, Aerospace science and technology 136, 108198 (2023). https://doi.org/10.1016/j.ast.2023.108198 [Google Scholar]
- H. Ozaki, T. Aoyagi, Prediction of steady flows passing fixed cylinders using deep learning, Scientific Reports 12, 447 (2022). https://doi.org/10.1038/s41598-021-03651-8 [Google Scholar]
- H. Gao, W. Qian, J. Dong, J. Liu, Rapid pre-diction of indoor airflow field using operator neural network with small dataset, Build-ing and Environment 251, 111175 (2024). https://doi.org/10.1016/j.buildenv.2024.111175 [Google Scholar]
- C. Wei, R. Ooka, Applying a physics-informed neural network to an indoor air-flow time-extrapolation prediction, Build-ing and Environment p. 113246 (2025). https://doi.org/10.1016/j.buildenv.2025.113246 [Google Scholar]
- T. Bashir, H. Wang, M. Tahir, Y. Zhang, Wind and solar power forecasting based on hy-brid cnn-abilstm, cnn-transformer-mlp mod-els, Renewable Energy 239, 122055 (2025). https://doi.org/10.1016/j.renene.2024.122055 [Google Scholar]
- X. Li, W. Xu, M. Ren, Y. Jiang, G. Fu, Hybrid cnn-lstm models for river flow pre-diction, Water Supply 22, 4902 (2022). https://doi.org/10.2166/ws.2022.170 [Google Scholar]
- Z. Li, T. Liu, W. Peng, Z. Yuan, J. Wang, A transformer-based neural operator for large-eddy simulation of turbulence, Physics of Fluids 36 (2024). https://doi.org/10.1063/5.0210493 [Google Scholar]
- Z. Liu, S. Zhang, X. Shao, Z. Wu, Accurate and efficient urban wind prediction at city-scale with memory-scalable graph neural network, Sus-tainable Cities and Society 99, 104935 (2023). https://doi.org/10.1016/j.scs.2023.104935 [Google Scholar]
- G. Chen, S. Chen, D. Li, C. Chen, A hy-brid deep learning air pollution prediction ap-proach based on neighborhood selection and spatio-temporal attention, Scientific Reports 15, 3685 (2025). https://doi.org/10.1038/s41598-025-88086-1 [Google Scholar]
- M. Jakubcová, P. Máca, M. Hanel, P. Pech, Com-bination of hybrid artificial neural networks with particle swarm optimization algorithm for spei forecasting, Journal of Hydrometeorology (2025). https://doi.org/10.1175/JHM-D-25-0034.1 [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.