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Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data

Published 30 Jan 2024 in cs.LG and cs.CV | (2401.16936v1)

Abstract: World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming. Wind energy has significant potential to not only reduce greenhouse emission, but also meet the ever increasing demand for energy. To enable the effective utilization of wind energy, addressing the following three challenges in wind data analysis is crucial. Firstly, improving data resolution in various climate conditions to ensure an ample supply of information for assessing potential energy resources. Secondly, implementing dimensionality reduction techniques for data collected from sensors/simulations to efficiently manage and store large datasets. Thirdly, extrapolating wind data from one spatial specification to another, particularly in cases where data acquisition may be impractical or costly. We propose a deep learning based approach to achieve multi-modal continuous resolution wind data prediction from discontinuous wind data, along with data dimensionality reduction.

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References (24)
  1. Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA, 2022. doi:10.1017/9781009157926. URL https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_FullReport.pdf.
  2. Mark C. Urban. Accelerating extinction risk from climate change. Science, 348(6234):571–573, 2015. doi:10.1126/science.aaa4984. URL https://www.science.org/doi/abs/10.1126/science.aaa4984.
  3. Over half of known human pathogenic diseases can be aggravated by climate change. Nature Climate Change, 12(9):869–875, August 2022. doi:10.1038/s41558-022-01426-1. URL https://doi.org/10.1038/s41558-022-01426-1.
  4. Extinction risk from climate change. Nature, 427(6970):145–148, January 2004. ISSN 0028-0836. doi:10.1038/nature02121.
  5. Rapid range shifts of species associated with high levels of climate warming. Science, 333(6045):1024–1026, 2011. doi:10.1126/science.1206432. URL https://www.science.org/doi/abs/10.1126/science.1206432.
  6. Multimodal deep learning for robust rgb-d object recognition. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), page 681–687. IEEE Press, 2015. doi:10.1109/IROS.2015.7353446. URL https://doi.org/10.1109/IROS.2015.7353446.
  7. Zhi-Jie Cao and Ge Gao. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nature Biotechnology, 40(10):1458–1466, May 2022. doi:10.1038/s41587-022-01284-4. URL https://doi.org/10.1038/s41587-022-01284-4.
  8. Mm-diffusion: Learning multi-modal diffusion models for joint audio and video generation. In CVPR, 2023.
  9. Hurricane forecasting: A novel multimodal machine learning framework. Weather and Forecasting, 37(6):817 – 831, 2022. doi:https://doi.org/10.1175/WAF-D-21-0091.1. URL https://journals.ametsoc.org/view/journals/wefo/37/6/WAF-D-21-0091.1.xml.
  10. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8628–8638, 2021.
  11. Enhanced deep residual networks for single image super-resolution. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017.
  12. Implicit diffusion models for continuous super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10021–10030, June 2023.
  13. Adversarial super-resolution of climatological wind and solar data. Proceedings of the National Academy of Sciences, 117(29):16805–16815, July 2020. doi:10.1073/pnas.1918964117. URL https://doi.org/10.1073/pnas.1918964117.
  14. Wisosuper: Benchmarking super-resolution methods on wind and solar data, 2021. URL https://arxiv.org/abs/2109.08770.
  15. Fourier neural operators for arbitrary resolution climate data downscaling, 2023. URL https://arxiv.org/abs/2305.14452.
  16. Reinstating continuous climate patterns from small and discretized data. In Synergy of Scientific and Machine Learning Modeling Workshop (SynS & ML) at ICML 2023, Honolulu, HI, 2023.
  17. Neural fields in visual computing and beyond, 2021. URL https://arxiv.org/abs/2111.11426.
  18. Super-resolution neural operator. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 18247–18256, June 2023.
  19. Reducing the dimensionality of data with neural networks. Science, 313(5786):504–507, 2006. doi:10.1126/science.1127647. URL https://www.science.org/doi/abs/10.1126/science.1127647.
  20. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, MLSDA’14, page 4–11, New York, NY, USA, 2014. Association for Computing Machinery. ISBN 9781450331593. doi:10.1145/2689746.2689747. URL https://doi.org/10.1145/2689746.2689747.
  21. iunets: learnable invertible up-and downsampling for large-scale inverse problems. In 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), pages 1–6. IEEE, 2020.
  22. Finding hidden patterns in high resolution wind flow model simulations. In Kothe Doug, Geist Al, Swaroop Pophale, Hong Liu, and Suzanne Parete-Koon, editors, Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, pages 345–365, Cham, 2022. Springer Nature Switzerland. ISBN 978-3-031-23606-8.
  23. The wind integration national dataset (wind) toolkit. Applied Energy, 151:355–366, 2015. ISSN 0306-2619. doi:https://doi.org/10.1016/j.apenergy.2015.03.121. URL https://www.sciencedirect.com/science/article/pii/S0306261915004237.
  24. Adam: A method for stochastic optimization, 2017.

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