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

Journal of Advances in Modeling Earth Systems (JAMES), 2020

25 February 2020

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

18 / 18 papers shown

Prediction performance of random reservoirs with different topology for nonlinear dynamical systems with different number of degrees of freedom

27 Nov 2025

Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence

260

21 Apr 2025

Minimum Reduced-Order Models via Causal Inference

Nan Chen

Honghu Liu

CML

172

29 Jun 2024

Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and ChallengesJournal of Advances in Modeling Earth Systems (JAMES), 2023

489

08 Jun 2023

Model scale versus domain knowledge in statistical forecasting of chaotic systemsPhysical Review Research (Phys. Rev. Res.), 2023

W. Gilpin

AI4TS

401

13 Mar 2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

245

20 Feb 2023

Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation

R. Parthipan

Damon J. Wischik

252

08 Oct 2022

A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic ParameterizationSocial Science Research Network (SSRN), 2022

Nan Chen

Yinling Zhang

CML

317

19 Aug 2022

Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 modelJournal of Advances in Modeling Earth Systems (JAMES), 2022

335

11 Aug 2022

Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers

Björn Lütjens

Catherine H. Crawford

224

23 Jul 2022

Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flowPNAS Nexus (PNAS Nexus), 2022

239

07 Jun 2022

Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 modelGeoscientific Model Development (GMD), 2022

339

28 Mar 2022

Learning Spatiotemporal Chaos Using Next-Generation Reservoir ComputingChaos (Chaos), 2022

W. A. S. Barbosa

D. Gauthier

347

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

237

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

301

01 Oct 2021

A Framework for Machine Learning of Model Error in Dynamical SystemsCommunications of the American Mathematical Society (Comm. Amer. Math. Soc.), 2021

Matthew E. Levine

Andrew M. Stuart

376

14 Jul 2021

Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers

261

16 Mar 2021

Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methodsProceedings of the Royal Society A (Proc. R. Soc. A), 2021

357

13 Feb 2021