Climate Modeling with Neural Diffusion Equations
暂无分享,去创建一个
Noseong Park | Dongeun Lee | Jeongwhan Choi | Kookjin Lee | Jeehyun Hwang | Hwangyong Choi | JeeHyun Hwang | Noseong Park | Dongeun Lee | Kookjin Lee | Jeongwhan Choi | Hwan-Kyu Choi
[1] Patrick Gallinari,et al. Deep learning for physical processes: incorporating prior scientific knowledge , 2017, ICLR.
[2] Xiaoyong Li,et al. Deep Learning-Based Weather Prediction: A Survey , 2021, Big Data Res..
[3] Jian Tang,et al. Continuous Graph Neural Networks , 2019, ICML.
[4] D. Freedman. Brownian motion and diffusion , 1971 .
[5] Renio S. Mendes,et al. Random Walks Associated with Nonlinear Fokker-Planck Equations , 2017, Entropy.
[6] Tao Ding,et al. Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach , 2018, Energies.
[7] Xin-She Yang,et al. Small‐world networks in geophysics , 2001, 1003.4886.
[8] J. Dormand,et al. A family of embedded Runge-Kutta formulae , 1980 .
[9] Stephan Rasp,et al. Neural networks for post-processing ensemble weather forecasts , 2018, Monthly Weather Review.
[10] Sushil J. Louis,et al. Forecasting the weather of Nevada: A deep learning approach , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[11] Jimeng Sun,et al. SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates , 2020, ICML.
[12] Zhouchen Lin,et al. Dissecting the Diffusion Process in Linear Graph Convolutional Networks , 2021, NeurIPS.
[13] Edward De Brouwer,et al. GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series , 2019, NeurIPS.
[14] École d'été de probabilités de Saint-Flour,et al. Differential equations driven by rough paths , 2007 .
[15] T. Prescott,et al. The brainstem reticular formation is a small-world, not scale-free, network , 2006, Proceedings of the Royal Society B: Biological Sciences.
[16] Tatsuaki Wada,et al. Discrete-time quantum walk with feed-forward quantum coin , 2013, Scientific Reports.
[17] David Duvenaud,et al. Neural Ordinary Differential Equations , 2018, NeurIPS.
[18] David Duvenaud,et al. Neural Networks with Cheap Differential Operators , 2019, NeurIPS.
[19] Terry Lyons,et al. Neural Controlled Differential Equations for Irregular Time Series , 2020, NeurIPS.
[20] Prabhat,et al. ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events , 2016, NIPS.
[21] Kilian Q. Weinberger,et al. Simplifying Graph Convolutional Networks , 2019, ICML.
[22] Prabhat,et al. Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets , 2016, ArXiv.
[23] S. Havlin,et al. Emergence of El Niño as an autonomous component in the climate network. , 2010, Physical review letters.
[24] K. Gurney,et al. Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence , 2008, PloS one.
[25] R. S. Mendes,et al. Nonlinear Kramers equation associated with nonextensive statistical mechanics. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[26] Fei Wang,et al. Neural Dynamics on Complex Networks , 2019, KDD.
[27] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[28] Jason Yosinski,et al. Hamiltonian Neural Networks , 2019, NeurIPS.
[29] Suleyman Serdar Kozat,et al. Spatio-temporal Weather Forecasting and Attention Mechanism on Convolutional LSTMs , 2021, ArXiv.
[30] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Yan Liu,et al. Differentiable Physics-informed Graph Networks , 2019, ArXiv.
[32] Yan Liu,et al. Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics , 2020, ICLR.
[33] Dit-Yan Yeung,et al. Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model , 2017, NIPS.
[34] P. Shrestha,et al. Understanding the Impacts of Climate Change , 2014 .
[35] Cyrus Shahabi,et al. Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning , 2018, SIGSPATIAL/GIS.
[36] Barbara Larwa. Heat Transfer Model to Predict Temperature Distribution in the Ground , 2018, Energies.
[37] J. Duncan,et al. Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE , 2020, ICML.
[38] Chaker El Amrani,et al. Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks , 2016 .
[39] A. Choudary,et al. Partial Differential Equations An Introduction , 2010, 1004.2134.
[40] Raia Hadsell,et al. Graph networks as learnable physics engines for inference and control , 2018, ICML.
[41] J. Dormand,et al. Numerical Methods for Differential Equations: A Computational Approach , 2017 .
[42] Andrew Gordon Wilson,et al. Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints , 2020, NeurIPS.
[43] Jan Peters,et al. Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning , 2019, ICLR.
[44] Hui Liu,et al. Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network , 2018 .
[45] Prabhat,et al. Exascale Deep Learning for Climate Analytics , 2018, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis.
[46] C. Tsallis,et al. From the nonlinear Fokker-Planck equation to the Vlasov description and back: Confined interacting particles with drag. , 2018, Physical review. E.
[47] Pengcheng Zhang,et al. Short-Term Rainfall Forecasting Using Multi-Layer Perceptron , 2020, IEEE Transactions on Big Data.
[48] Paris Perdikaris,et al. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..
[49] Ashesh Chattopadhyay,et al. Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data , 2020, Scientific Reports.
[50] Linpeng Huang,et al. A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations , 2018, AAAI.
[51] Steven R. Hanna,et al. Handbook on atmospheric diffusion , 1982 .
[52] Miles Cranmer,et al. Lagrangian Neural Networks , 2020, ICLR 2020.
[53] Sebastian Scher,et al. Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning , 2018, Geophysical Research Letters.
[54] Jinfu Chen,et al. Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach , 2018 .
[55] Thomas F. Stocker,et al. Introduction to Climate Modelling , 2011 .