Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction

24 April 2019

Papers citing "Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction"

9 / 9 papers shown

Title
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
Variability of echo state network prediction horizon for partially observed dynamical systems Ajit Mahata Reetish Padhi A. Apte 21 1 0 19 Jun 2023
Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction Taehyeon Kim Namgyu Ho Donggyu Kim Se-Young Yun BDL 20 6 0 30 Jun 2022
Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case R. Mojgani A. Chattopadhyay P. Hassanzadeh 27 15 0 01 Oct 2021
A comparison of combined data assimilation and machine learning methods for offline and online model error correction A. Farchi Marc Bocquet P. Laloyaux Massimo Bonavita Quentin Malartic OffRL 25 35 0 23 Jul 2021
Bridging observation, theory and numerical simulation of the ocean using Machine Learning Maike Sonnewald Redouane Lguensat Daniel C. Jones P. Dueben J. Brajard Venkatramani Balaji AI4Cl AI4CE 46 100 0 26 Apr 2021
Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution Omer San Adil Rasheed T. Kvamsdal 48 50 0 26 Mar 2021
Using machine learning to correct model error in data assimilation and forecast applications A. Farchi P. Laloyaux Massimo Bonavita Marc Bocquet AI4CE 28 101 0 23 Oct 2020
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM A. Chattopadhyay P. Hassanzadeh D. Subramanian AI4CE 11 40 0 20 Jun 2019