A comparison of combined data assimilation and machine learning methods for offline and online model error correction

23 July 2021

Papers citing "A comparison of combined data assimilation and machine learning methods for offline and online model error correction"

7 / 7 papers shown

Title
CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation Chuanqi Chen Nan Chen Yinling Zhang Jin-Long Wu AI4CE 36 2 0 26 Oct 2024
Online model error correction with neural networks: application to the Integrated Forecasting System A. Farchi M. Chrust Marc Bocquet Massimo Bonavita 21 0 0 06 Mar 2024
Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification M. Sacco J. J. Ruiz M. Pulido P. Tandeo UQCV 25 10 0 29 Nov 2021
Integrating Recurrent Neural Networks with Data Assimilation for Scalable Data-Driven State Estimation S. Penny T. A. Smith Tse-Chun Chen Jason A. Platt Hsin-Yi Lin M. Goodliff H. Abarbanel AI4CE 22 42 0 25 Sep 2021
State, global and local parameter estimation using local ensemble Kalman filters: applications to online machine learning of chaotic dynamics Quentin Malartic A. Farchi Marc Bocquet 36 19 0 23 Jul 2021
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems Alexander Wikner Jaideep Pathak Brian Hunt M. Girvan T. Arcomano I. Szunyogh Andrew Pomerance Edward Ott AI4CE 64 70 0 10 Feb 2020
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model D. Gagne H. Christensen A. Subramanian A. Monahan AI4CE BDL 44 139 0 10 Sep 2019