AttGAN: attention gated generative adversarial network for spatio-temporal super-resolution of ocean phenomena

References

• Agrawal, P., N. Katal, and N. Hooda. 2022. “Segmentation and Classification of Brain Tumor Using 3D-UNet Deep Neural Networks.” International Journal of Cognitive Computing in Engineering 3:199–210. https://doi.org/10.1016/j.ijcce.2022.11.001.
   Google Scholar
• Arora, R. 2022. “PhySRNet: Physics Informed Super-Resolution Network for Application in Computational Solid Mechanics”. In 2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S), 13–18. IEEE. https://doi.org/10.1109/ai4s56813.2022.00008.
   Google Scholar
• Br, P., and N. Rajkumar. 2024. “Real-Time Intelligent Video Surveillance System Using Recurrent Neural Network.” Procedia Computer Science 235:1522–1531. https://doi.org/10.1016/j.procs.2024.04.143.
   Google Scholar
• Burns, K. J., G. M. Vasil, J. S. Oishi, D. Lecoanet, and B. P. Brown. 2020. “Dedalus: A Flexible Framework for Numerical Simulations with Spectral Methods.” Physical Review Research 2 (2): 023068. https://doi.org/10.1103/physrevresearch.2.023068.
   Google Scholar
• Jiang C., S. Esmaeilzadeh, K. Azizzadenesheli, K. Kashinath, M. Mustafa, H. A. Tchelepi, P. Marcus, M. Prabhat, and A. Anandkumar. 2020. “Meshfreeflownet: A Physics-Constrained Deep Continuous Space–Time Super-Resolution Framework.” In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, 1–15. IEEE. https://doi.org/10.1109/sc41405.2020.00013.
   Google Scholar
• Chakraborty, T., U. R. KS, S. M. Naik, M. Panja, and B. Manvitha. 2024. “Ten Years of Generative Adversarial Nets (GANs): A Survey of the State-of-the-Art.” Machine Learning: Science and Technology5 (1): 011001. https://doi.org/10.1088/2632-2153/ad1f77.
   Google Scholar
• Cole, E. K., F. Ong, S. S. Vasanawala, and J. M. Pauly. 2021. “Fast Unsupervised MRI Reconstruction Without Fully-Sampled Ground Truth Data Using Generative Adversarial Networks”. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 3988–3997. https://doi.org/10.1109/iccvw54120.2021.00444.
   Google Scholar
• Edwards, C. A., A. M. Moore, I. Hoteit, and B. D. Cornuelle. 2015. “Regional Ocean Data Assimilation.” Annual Review of Marine Science 7 (1): 21–42. https://doi.org/10.1146/annurev-marine-010814-015821.
   PubMedGoogle Scholar
• Fujii, Y., E. Rémy, H. Zuo, P. Oke, G. Halliwell, F. Gasparin, M. Benkiran. 2019. “Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks.” Frontiers in Marine Science 6:417. https://doi.org/10.3389/fmars.2019.00417.
   Web of Science ®Google Scholar
• Gao, H., L. Sun, and J.-X. Wang. 2021. “Super-Resolution and Denoising of Fluid Flow Using Physics-Informed Convolutional Neural Networks Without High-Resolution Labels.” Physics of Fluids 33 (7): 073603. https://doi.org/10.1063/5.0054312.
   Web of Science ®Google Scholar
• Giang, T. L., K. B. Dang, Q. T. Le, V. G. Nguyen, S. S. Tong, and V. -M. Pham. 2020. “U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery.” IEEE Access 8:186257–186273. https://doi.org/10.1109/access.2020.3030112.
   Google Scholar
• Glawion, L., J. Polz, H. G. Kunstmann, B. Fersch, and C. Chwala. 2023. “spateGAN: Spatio-Temporal Downscaling of Rainfall Fields using a cGAN Approach”. https://doi.org/10.22541/essoar.167690003.33629126/v1.
   Google Scholar
• Guo, J., and H. Chao. 2017. “Building an End-To-End Spatial-Temporal Convolutional Network for Video Super-Resolution”. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31. https://doi.org/10.1609/aaai.v31i1.11228.
   Google Scholar
• Guo, S., N. Sun, Y. Pei, and Q. Li. 2023. “3D-UNet-LSTM: A Deep Learning-Based Radar Echo Extrapolation Model for Convective Nowcasting.” Remote Sensing 15 (6): 1529. https://doi.org/10.3390/rs15061529.
   Web of Science ®Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778. https://doi.org/10.1109/cvpr.2016.90.
   Google Scholar
• Huang, G., Z. Liu, L. Van Der Maaten, and K. Q. Weinberger. 2017. “Densely Connected Convolutional Networks”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708. https://doi.org/10.1109/cvpr.2017.243.
   Google Scholar
• Ismail, M., C. Shang, J. Yang, and Q. Shen. 2023. “Sparse Data-Based Image Super-Resolution with ANFIS Interpolation.” Neural Computing and Applications 35: 7221–7233. https://doi.org/10.1007/s00521-021-06500-x.
   Web of Science ®Google Scholar
• Jagtap, A. D., and G. E. Karniadakis. 2021. “Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations”. In AAAI Spring Symposium: MLPS, Vol. 10. https://doi.org/10.4208/cicp.oa-2020-0164.
   Google Scholar
• Jagtap, A. D., E. Kharazmi, and G. E. Karniadakis. 2020. “Conservative Physics-Informed Neural Networks on Discrete Domains for Conservation Laws: Applications to Forward and Inverse Problems.” Computer Methods in Applied Mechanics and Engineering 365:113028. https://doi.org/10.1016/j.cma.2020.113028.
   Web of Science ®Google Scholar
• Jia, S., D. Bi, J. Liao, S. Jiang, M. Xu, and S. Zhang. 2023. “Structure-Adaptive Convolutional Neural Network for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 61: 1–16. https://doi.org/10.1109/tgrs.2023.3326231.
   Web of Science ®Google Scholar
• Kappeler, A., S. Yoo, Q. Dai, and A. K. Katsaggelos. 2016. “Video Super-Resolution with Convolutional Neural Networks.” IEEE Transactions on Computational Imaging 2 (2): 109–122. https://doi.org/10.1109/tci.2016.2532323.
   Web of Science ®Google Scholar
• Kearney, V., J. W. Chan, T. Wang, A. Perry, M. Descovich, O. Morin, S. S. Yom, and T. D. Solberg. 2020. “DoseGAN: A Generative Adversarial Network for Synthetic Dose Prediction Using Attention-Gated Discrimination and Generation.” Scientific Reports 10 (1): 11073. https://doi.org/10.1038/s41598-020-68062-7.
   PubMed Web of Science ®Google Scholar
• Kharazmi, E., Z. Zhang, and G. E. Karniadakis. 2021. “hp-VPINNs: Variational Physics-Informed Neural Networks with Domain Decomposition.” Computer Methods in Applied Mechanics and Engineering374:113547. https://doi.org/10.1016/j.cma.2020.113547.
   Web of Science ®Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” Advances in Neural Information Processing Systems 60: 84–90. https://doi.org/10.1145/3065386.
   Google Scholar
• Ledig, C., L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, et al. 2017. “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4681–4690. https://doi.org/10.1109/cvpr.2017.19.
   Google Scholar
• Li, M., and C. McComb. 2022. “Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations.” Journal of Computing and Information Science in Engineering 22 (4): 044501. https://doi.org/10.31224/osf.io/pu2jx.
   Web of Science ®Google Scholar
• Lim, B., S. Son, H. Kim, S. Nah, and K. Mu Lee. 2017. “Enhanced Deep Residual Networks for Single Image Super-Resolution”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 136–144. https://doi.org/10.1109/cvprw.2017.151.
   Google Scholar
• Ling, F., Y. Du, X. Li, W. Li, F. Xiao, and Y. Zhang. 2013. “Interpolation-Based Super-Resolution Land Cover Mapping.” Remote Sensing Letters 4 (7): 629–638. https://doi.org/10.1080/2150704x.2013.781284.
   Web of Science ®Google Scholar
• Marechal, G., and F. Ardhuin. 2021. “Surface Currents and Significant Wave Height Gradients: Matching Numerical Models and High-Resolution Altimeter Wave Heights in the Agulhas Current Region.” Journal of Geophysical Research: Oceans 126 (2): e2020JC016564. https://doi.org/10.1002/essoar.10505343.1.
   Web of Science ®Google Scholar
• Nguyen, T. T., T. N. Chi, M. D. Hoang, H. N. Thai, and T. N. Duc. 2020. “3D Unet Generative Adversarial Network for Attenuation Correction of SPECT Images”. In 2020 4th International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom), 93–97. IEEE. https://doi.org/10.1109/sigtelcom49868.2020.9199018.
   Google Scholar
• Parker, J. A., R. V. Kenyon, and D. E. Troxel. 1983. “Comparison of Interpolating Methods for Image Resampling.” IEEE Transactions on Medical Imaging 2 (1): 31–39. https://doi.org/10.1109/TMI.1983.4307610.
   PubMedGoogle Scholar
• Qiao, F., W. Zhao, X. Yin, X. Huang, X. Liu, Q. Shu, G. Wang, et al. 2016. “A Highly Effective Global Surface Wave Numerical Simulation with Ultra-High Resolution”. In SC'16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 46–56. IEEE. https://doi.org/10.1109/sc.2016.4.
   Google Scholar
• Raissi, M., P. Perdikaris, and G. E. Karniadakis. 2019. “Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations.” Journal of Computational Physics 378:686–707. https://doi.org/10.1016/j.jcp.2018.10.045.
   Web of Science ®Google Scholar
• Song, J., H. Yi, W. Xu, X. Li, B. Li, and Y. Liu. 2023. “ESRGAN-DP: Enhanced Super-Resolution Generative Adversarial Network with Adaptive Dual Perceptual Loss.” Heliyon 9 (4): e15134. https://doi.org/10.1016/j.heliyon.2023.e15134.
   PubMed Web of Science ®Google Scholar
• Velho, H. F. C., H. C. Furtado, S. B. Sambatti, C. B. O. F. de Barros, M. E. Welter, R. P. Souto, D. Carvalho, and D. O. Cardoso. 2022. “Data Assimilation by Neural Network for Ocean Circulation: Parallel Implementation.” Supercomputing Frontiers and Innovations 9 (1): 74–86. https://doi.org/10.14529/jsfi220105.
   Google Scholar
• Wang, T.-C., M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro. 2018. “High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8798–8807. https://doi.org/10.1109/cvpr.2018.00917.
   Google Scholar
• Wang, C., Y. Zhang, Y. Zhang, R. Tian, and M. Ding. 2021. “Mars Image Super-Resolution Based on Generative Adversarial Network.” IEEE Access 9:108889–108898. https://doi.org/10.1109/access.2021.3101858.
   Google Scholar
• Wang, X., S. Zhu, Y. Guo, P. Han, Y. Wang, Z. Wei, and X. Jin. 2022. “TransFlowNet: A Physics-Constrained Transformer Framework for Spatio-Temporal Super-Resolution of Flow Simulations.” Journal of Computational Science 65:101906. https://doi.org/10.1016/j.jocs.2022.101906.
   Web of Science ®Google Scholar
• Waters, J., D. J. Lea, M. J. Martin, I. Mirouze, A. Weaver, and J. While. 2015. “Implementing a Variational Data Assimilation System in An Operational 1/4 Degree Global Ocean Model.” Quarterly Journal of the Royal Meteorological Society 141 (687): 333–349. https://doi.org/10.1002/qj.2388.
   Web of Science ®Google Scholar
• Xie, Y., E. Franz, M. Chu, and N. Thuerey. 2018. “tempoGAN: A Temporally Coherent, Volumetric GAN for Super-Resolution Fluid Flow.” ACM Transactions on Graphics (TOG) 37 (4): 1–15. https://doi.org/10.1145/3197517.3201304.
   Web of Science ®Google Scholar
• Xie, S., R. Girshick, P. Dollár, Z. Tu, and K. He. 2017. “Aggregated Residual Transformations for Deep Neural Networks”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1492–1500 . https://doi.org/10.1109/cvpr.2017.634.
   Google Scholar
• Xu, J., Y. Chae, B. Stenger, and A. Datta. 2018. “Dense Bynet: Residual Dense Network for Image Super Resolution”. In 2018 25th IEEE International Conference on Image Processing (ICIP), 71–75. IEEE. https://doi.org/10.1109/icip.2018.8451696.
   Google Scholar
• Yonekura, K., K. Maruoka, K. Tyou, and K. Suzuki. 2023. “Super-Resolving 2D Stress Tensor Field Conserving Equilibrium Constraints Using Physics-Informed U-Net.” Finite Elements in Analysis and Design 213:103852. https://doi.org/10.1016/j.finel.2022.103852.
   Web of Science ®Google Scholar
• Yoon, Y., H.-G. Jeon, D. Yoo, J.-Y. Lee, and I. So Kweon. 2015. “Learning a Deep Convolutional Network for Light-Field Image Super-Resolution”. In Proceedings of the IEEE International Conference on Computer Vision Workshops, 24–32. https://doi.org/10.1109/iccvw.2015.17.
   Google Scholar
• Yu, L., X. Yang, H. Chen, J. Qin, and P. A. Heng. 2017. “Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images”. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31. https://doi.org/10.1609/aaai.v31i1.10510.
   Google Scholar
• Yuan, C., R. Grimshaw, and E. Johnson. 2018. “The Evolution of Second Mode Internal Solitary Waves Over Variable Topography.” Journal of Fluid Mechanics 836:238–259. https://doi.org/10.1017/jfm.2017.812.
   Web of Science ®Google Scholar
• Zayats, M., M. J. Zimoń, K. Yeo, and S. Zhuk. 2022. “Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks”. In 2022 IEEE 61st Conference on Decision and Control (CDC), 3377–3382. IEEE. https://doi.org/10.1109/cdc51059.2022.9992729.
   Google Scholar
• Zhang, K., L. V. Gool, and R. Timofte. 2020. “Deep Unfolding Network for Image Super-Resolution”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3217–3226. https://doi.org/10.1109/cvpr42600.2020.00328.
   Google Scholar
• Zhang, D., L. Guo, and G. E. Karniadakis. 2020. “Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks.” SIAM Journal on Scientific Computing 42 (2): A639–A665. https://doi.org/10.1137/19m1260141.
   Web of Science ®Google Scholar
• Zhang, Y., C. Liu, M. Liu, T. Liu, H. Lin, C.-B. Huang, and L. Ning. 2024. “Attention is All You Need: Utilizing Attention in AI-Enabled Drug Discovery.” Briefings in Bioinformatics 25 (1): bbad467. https://doi.org/10.1093/bib/bbad467.
   Web of Science ®Google Scholar