CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation

26 October 2024

Papers citing "CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation"

26 / 26 papers shown

Title
Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator Xinghao Dong Chuanqi Chen Jin-Long Wu DiffM AI4CE 61 5 0 06 Aug 2024
Limits and Powers of Koopman Learning Matthew J. Colbrook Igor Mezić Alexei Stepanenko 49 10 0 08 Jul 2024
CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation Chuanqi Chen Nan Chen Jin-Long Wu AI4CE 57 4 0 10 Apr 2024
Learning About Structural Errors in Models of Complex Dynamical Systems Jin-Long Wu Matthew E. Levine Tapio Schneider Andrew M. Stuart AI4CE 59 17 0 29 Dec 2023
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator Shaowu Pan E. Kaiser Brian M. de Silva J. Nathan Kutz Steven L. Brunton 48 8 0 22 Jun 2023
CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting Chuanqi Chen Nan Chen Jin-Long Wu 55 9 0 16 Apr 2023
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review Sibo Cheng César Quilodrán-Casas Said Ouala A. Farchi Che Liu ... Weiping Ding Yike Guo A. Carrassi Marc Bocquet Rossella Arcucci AI4CE 51 128 0 18 Mar 2023
Online model error correction with neural networks in the incremental 4D-Var framework A. Farchi M. Chrust Marc Bocquet P. Laloyaux Massimo Bonavita 89 20 0 25 Oct 2022
A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization Nan Chen Yinling Zhang CML 45 15 0 19 Aug 2022
Conditional Gaussian Nonlinear System: a Fast Preconditioner and a Cheap Surrogate Model For Complex Nonlinear Systems N. Chen Yingda Li Honghu Liu 42 17 0 09 Dec 2021
Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations Georg Gottwald Sebastian Reich AI4CE 57 38 0 08 Aug 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 56 19 0 23 Jul 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 49 36 0 23 Jul 2021
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics Guy Revach Nir Shlezinger Xiaoyong Ni Adrià López Escoriza Ruud J. G. van Sloun Yonina C. Eldar 43 273 0 21 Jul 2021
Modern Koopman Theory for Dynamical Systems Steven L. Brunton M. Budišić E. Kaiser J. Nathan Kutz AI4CE 81 411 0 24 Feb 2021
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components Alexander Wikner Jaideep Pathak Brian R. Hunt I. Szunyogh M. Girvan Edward Ott 14 37 0 15 Feb 2021
Data Assimilation Networks Pierre Boudier Anthony Fillion Serge Gratton S. Gürol Sixin Zhang AI4CE 35 11 0 19 Oct 2020
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 367 2,355 0 18 Oct 2020
ERFit: Entropic Regression Fit Matlab Package, for Data-Driven System Identification of Underlying Dynamic Equations A. AlMomani Erik Bollt 8 7 0 06 Oct 2020
Online learning of both state and dynamics using ensemble Kalman filters Marc Bocquet A. Farchi Quentin Malartic 39 28 0 06 Jun 2020
Learning Stochastic Closures Using Ensemble Kalman Inversion T. Schneider Andrew M. Stuart Jin-Long Wu 49 40 0 17 Apr 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 118 2,082 0 08 Oct 2019
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model D. Gagne H. Christensen A. Subramanian A. Monahan AI4CE BDL 92 141 0 10 Sep 2019
Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability Shaowu Pan Karthik Duraisamy 45 137 0 09 Jun 2019
Deep learning to represent sub-grid processes in climate models S. Rasp Michael S. Pritchard Pierre Gentine AI4Cl AI4CE 27 727 0 12 Jun 2018
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data Anuj Karpatne G. Atluri James H. Faghmous M. Steinbach A. Banerjee A. Ganguly Shashi Shekhar N. Samatova Vipin Kumar AI4CE 31 978 0 27 Dec 2016