Citation: | Han Yang, Zhu Junpeng, Guo Chunyu, Fan Yiwei, Wang Yonghao. A flow field super-resolution reconstruction method based on diffusion model. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(10): 2309-2320. DOI: 10.6052/0459-1879-23-167 |
[1] |
Yang L, Zhang ZL, Song Y, et al. Diffusion models: A comprehensive survey of methods and applications. arXiv, 2022: 2209.00796
|
[2] |
Yu C, Bi X, Fan Y. Deep learning for fluid velocity field estimation: A review. Ocean Engineering, 2023, 271: 113693 doi: 10.1016/j.oceaneng.2023.113693
|
[3] |
陈皓, 郭明明, 田野等. 卷积神经网络在流场重构研究中的进展. 力学学报, 2022, 54(9): 2343-2360 (Chen Hao, Guo Mingming, Tian Ye, et al. Progress of convolutional neural network in flow field reconstruction. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(9): 2343-2360 (in Chinese)
Chen Hao, Guo Mingming, Tian Ye, et al. Progress of Convolutional Neural Network in Flow Field Reconstruction. Chinese Journal of Mechanics, 2022, 54(9): 2343-2360(in Chinese))
|
[4] |
Guo X, Li W, Iorio F. Convolutional neural networks for steady flow approximation//22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco. Assoc Computing Machinery, 2016: 481-490
|
[5] |
Lee Y, Yang H, Yin Z. PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry. Experiments in Fluids, 2017, 58: 1-10 doi: 10.1007/s00348-016-2278-6
|
[6] |
Cai S, Liang J, Gao Q, et al. Particle image velocimetry based on a deep learning motion estimator. IEEE Transactions on Instrumentation and Measurement, 2019, 69(6): 3538-3554
|
[7] |
Lagemann C, Lagemann K, Mukherjee S, et al. Deep recurrent optical flow learning for particle image velocimetry data. Nature Machine Intelligence, 2021, 3(7): 641-651 doi: 10.1038/s42256-021-00369-0
|
[8] |
Dong C, Loy CC, He K, et al. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(2): 295-307
|
[9] |
Kim J, Lee J K, Lee KM. Accurate image super-resolution using very deep convolutional networks//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 29th IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas. Institute of Electrical and Electronics Engineers Inc, 2016: 1646-1654
|
[10] |
Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu. Institute of Electrical and Electronics Engineers Inc, 2017: 4681-4690
|
[11] |
Tong T, Li G, Liu X, et al. Image super-resolution using dense skip connections//Proceedings of the IEEE international conference on computer vision, 16th IEEE International Conference on Computer Vision, Venice. Institute of Electrical and Electronics Engineers Inc, 2017: 4799-4807
|
[12] |
Zhang Y, Li K, Li K, et al. Image super-resolution using very deep residual channel attention networks//Lecture Notes in Computer Science, 15th European Conference on Computer Vision (ECCV), Munich. Springer Verlag, 2018: 286-301
|
[13] |
Lee J, Lee S, You D. Deep learning approach in multi-scale prediction of turbulent mixing-layer. arXiv, 2018: 1809.07021
|
[14] |
Deng Z, He C, Liu Y, et al. Super-resolution reconstruction of turbulent velocity fields using a generative adversarial network-based artificial intelligence framework. Physics of Fluids, 2019, 31(12): 125111 doi: 10.1063/1.5127031
|
[15] |
Wang X, Yu K, Wu S, et al. Esrgan: Enhanced super-resolution generative adversarial networks//Lecture Notes in Computer Science, 15th European Conference on Computer Vision, Munich, 2018. Springer Verlag, 2019: 63-79
|
[16] |
Fukami K, Fukagata K, Taira K. Super-resolution reconstruction of turbulent flows with machine learning. Journal of Fluid Mechanics, 2019, 870: 106-120 doi: 10.1017/jfm.2019.238
|
[17] |
Liu B, Tang J, Huang H, et al. Deep learning methods for super-resolution reconstruction of turbulent flows. Physics of Fluids, 2020, 32(2): 025105 doi: 10.1063/1.5140772
|
[18] |
Kong C, Chang JT, Li YF, et al. Deep learning methods for super-resolution reconstruction of temperature fields in a supersonic combustor. AIP Advances, 2020, 10(11): 115021 doi: 10.1063/5.0030040
|
[19] |
Ferdian E, Suinesiaputra A, Dubowitz DJ, et al. 4 DFlowNet: super-resolution 4D flow MRI using deep learning and computational fluid dynamics. Frontiers in Physics, 2020, 8: 138 doi: 10.3389/fphy.2020.00138
|
[20] |
Fukami K, Fukagata K, Taira K. Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows. Journal of Fluid Mechanics, 2021, 909: A9 doi: 10.1017/jfm.2020.948
|
[21] |
Bi X, Liu A, Fan Y, et al. FlowSRNet: A multi-scale integration network for super-resolution reconstruction of fluid flows. Physics of Fluids, 2022, 34(12): 127104 doi: 10.1063/5.0128435
|
[22] |
Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, 378: 686-707 doi: 10.1016/j.jcp.2018.10.045
|
[23] |
Raissi M, Yazdani A, Karniadakis GE. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science, 2020, 367(6481): 1026-1030 doi: 10.1126/science.aaw4741
|
[24] |
Shu D, Li Z, Farimani AB. A physics-informed diffusion model for high-fidelity flow field reconstruction. Journal of Computational Physics, 2023, 478: 111972 doi: 10.1016/j.jcp.2023.111972
|
[25] |
Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 2020, 33: 6840-6851
|
[26] |
Song Y, Sohl-Dickstein J, Kingma DP, et al. Score-based generative modeling through stochastic differential equations. arXiv, 2020: 2011.13456
|
[27] |
Saharia C, Ho J, Chan W, et al. Image super-resolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(4): 4713-4726
|
[28] |
Li H, Yang Y, Chang M, et al. Srdiff: Single image super-resolution with diffusion probabilistic models. Neurocomputing, 2022, 479: 47-59 doi: 10.1016/j.neucom.2022.01.029
|
[29] |
Liu J, Tang J, Wu G. Residual feature distillation network for lightweight image super-resolution//Computer Vision–ECCV 2020 Workshops, Glasgow, 2020. Springer International Publishing, 2020: 41-55
|
[30] |
Lu C, Zhou Y, Bao F, et al. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. arXiv, 2022: 2206.00927
|
[31] |
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//Proceedings of the 31st International Conference on Neural Information Processing Systems, December 2017: 6000-6010
|
[32] |
Carlier J. Second set of fluid mechanics image sequences. European Project Fluid Image Analysis and Description, Project No. FP6-513663, 2005
|
[33] |
Resseguier V, Mémin E, Chapron B. Geophysical flows under location uncertainty, Part II: Quasi-geostrophy and efficient ensemble spreading. Geophysical & Astrophysical Fluid Dynamics, 2017, 111(3): 177-208
|
[34] |
Li Y, Perlman E, Wan M, et al. A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence. Journal of Turbulence, 2008, 9: N31
|
[35] |
Song J, Meng C, Ermon S. Denoising diffusion implicit models. arXiv, 2020: 2010.02502
|
[36] |
Keys R. Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981, 29(6): 1153-1160 doi: 10.1109/TASSP.1981.1163711
|
1. |
江巍,尹豪,吴剑,汤艳春,李坤鹏,郑宏. 基于S-R和分解定理的二维几何非线性问题的虚单元法求解. 工程力学. 2024(08): 23-35 .
![]() | |
2. |
宋彦琦,石博康,李向上. 基于S-R和分解定理的无网格法在功能梯度板中的应用. 上海大学学报(自然科学版). 2022(04): 702-714 .
![]() | |
3. |
杨健生,曾治平,韦冬炎,彭林欣. 基于无网格法的非均匀弹性地基上变厚度加筋板弯曲与固有频率分析. 计算力学学报. 2021(03): 364-370 .
![]() | |
4. |
覃霞,刘珊珊,谌亚菁,彭林欣. 基于遗传算法的弹性地基加肋板肋梁无网格优化分析. 力学学报. 2020(01): 93-110 .
![]() | |
5. |
肖国峰. 具有稳定数值解的三维谐振子. 计算力学学报. 2020(01): 119-130 .
![]() | |
6. |
王莉华,李溢铭,褚福运. 基于分区径向基函数配点法的大变形分析. 力学学报. 2019(03): 743-753 .
![]() | |
7. |
刘斌,李冬明,谢佳萱. 非保守荷载大变形分析的无网格方法. 武汉理工大学学报. 2019(09): 71-77 .
![]() |