Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods

13 February 2021

Papers citing "Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods"

8 / 8 papers shown

Title
Recurrent Deep Kernel Learning of Dynamical Systems N. Botteghi Paolo Motta Andrea Manzoni P. Zunino Mengwu Guo 33 1 0 30 May 2024
Computational Hypergraph Discovery, a Gaussian Process framework for connecting the dots Théo Bourdais Pau Batlle Xianjin Yang Ricardo Baptista Nicolas Rouquette H. Owhadi 21 0 0 28 Nov 2023
One-Shot Learning of Stochastic Differential Equations with Data Adapted Kernels Matthieu Darcy B. Hamzi Giulia Livieri H. Owhadi P. Tavallali 36 26 0 24 Sep 2022
Gaussian Process Hydrodynamics H. Owhadi 26 1 0 21 Sep 2022
Ensemble forecasts in reproducing kernel Hilbert space family Benjamin Dufée Berenger Hug É. Mémin G. Tissot 24 1 0 29 Jul 2022
Deep learning for surrogate modelling of 2D mantle convection Siddhant Agarwal Nicola Tosi Pan Kessel D. Breuer G. Montavon AI4TS AI4CE 28 7 0 23 Aug 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,683 0 05 Dec 2016
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung W. Wong W. Woo 239 7,916 0 13 Jun 2015