Data-driven super-parameterization using deep learning: Experimentation
with multi-scale Lorenz 96 systems and transfer-learning

Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning

25 February 2020

Ashesh Chattopadhyay

Pedram Hassanzadeh

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Papers citing "Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning"

11 / 11 papers shown

Title
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges Karan Jakhar Yifei Guan R. Mojgani Ashesh Chattopadhyay Pedram Hassanzadeh AI4Cl AI4CE 75 16 0 08 Jun 2023
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles R. Maulik Romain Egele Krishnan Raghavan Prasanna Balaprakash UQCV AI4TS AI4CE 59 6 0 20 Feb 2023
A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization Nan Chen Yinling Zhang CML 68 15 0 19 Aug 2022
Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model Redouane Lguensat Julie Deshayes Homer Durand Venkatramani Balaji 96 4 0 11 Aug 2022
Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers Björn Lütjens Catherine H. Crawford C. Watson C. Hill Dava Newman AI4CE 52 10 0 23 Jul 2022
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow Adam Subel Yifei Guan Ashesh Chattopadhyay Pedram Hassanzadeh AI4CE 59 43 0 07 Jun 2022
Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing W. A. S. Barbosa D. Gauthier 88 35 0 24 Mar 2022
Conditional Gaussian Nonlinear System: a Fast Preconditioner and a Cheap Surrogate Model For Complex Nonlinear Systems N. Chen Yingda Li Honghu Liu 69 18 0 09 Dec 2021
Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case R. Mojgani Ashesh Chattopadhyay Pedram Hassanzadeh 87 16 0 01 Oct 2021
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers Ashesh Chattopadhyay M. Mustafa Pedram Hassanzadeh Eviatar Bach K. Kashinath AI4CE 85 25 0 16 Mar 2021
Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods B. Hamzi R. Maulik H. Owhadi AI4TS 70 29 0 13 Feb 2021