Deep learning to represent sub-grid processes in climate models

Deep learning to represent sub-grid processes in climate models

12 June 2018

Michael S. Pritchard

Papers citing "Deep learning to represent sub-grid processes in climate models"

9 / 9 papers shown

Title
CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints Xin Wang Juntao Yang Jeff Adie Simon See Kalli Furtado Chen Chen T. Arcomano R. Maulik G. Mengaldo AI4CE 54 0 0 18 Feb 2025
CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation Chuanqi Chen Nan Chen Yinling Zhang Jin-Long Wu AI4CE 59 2 0 26 Oct 2024
Generating Physical Dynamics under Priors Zihan Zhou Xiaoxue Wang Tianshu Yu DiffM AI4CE 82 0 0 01 Sep 2024
Enhancing Low-Order Discontinuous Galerkin Methods with Neural Ordinary Differential Equations for Compressible Navier--Stokes Equations Shinhoo Kang Emil M. Constantinescu AI4CE 38 0 0 29 Oct 2023
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations T. Schneider Shiwei Lan Andrew M. Stuart J. Teixeira AI4Cl 43 315 0 31 Aug 2017
Deep Learning for Computational Chemistry Garrett B. Goh Nathan Oken Hodas Abhinav Vishnu AI4CE 50 674 0 17 Jan 2017
TensorFlow: A system for large-scale machine learning Martín Abadi P. Barham Jianmin Chen Zhiwen Chen Andy Davis ... Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu Xiaoqiang Zhang GNN AI4CE 329 18,300 0 27 May 2016
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 290 43,154 0 11 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 776 149,474 0 22 Dec 2014