Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case

1 October 2021

Papers citing "Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case"

5 / 5 papers shown

Title
Interpretable structural model error discovery from sparse assimilation increments using spectral bias-reduced neural networks: A quasi-geostrophic turbulence test case R. Mojgani A. Chattopadhyay P. Hassanzadeh 33 7 0 22 Sep 2023
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges Karan Jakhar Yifei Guan R. Mojgani A. Chattopadhyay P. Hassanzadeh AI4Cl AI4CE 22 14 0 08 Jun 2023
CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting Chuanqi Chen Nan Chen Jin-Long Wu 29 9 0 16 Apr 2023
Online model error correction with neural networks in the incremental 4D-Var framework A. Farchi M. Chrust Marc Bocquet P. Laloyaux Massimo Bonavita 48 17 0 25 Oct 2022
Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems A. Chattopadhyay Ebrahim Nabizadeh Eviatar Bach P. Hassanzadeh AI4CE 19 20 0 09 Jun 2022