248 related articles for article (PubMed ID: 32237755)
1. Predicting observed and hidden extreme events in complex nonlinear dynamical systems with partial observations and short training time series.
Chen N; Majda AJ
Chaos; 2020 Mar; 30(3):033101. PubMed ID: 32237755
[TBL] [Abstract][Full Text] [Related]
2. Model Error, Information Barriers, State Estimation and Prediction in Complex Multiscale Systems.
Majda AJ; Chen N
Entropy (Basel); 2018 Aug; 20(9):. PubMed ID: 33265733
[TBL] [Abstract][Full Text] [Related]
3. Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification.
Chen N; Majda AJ
Entropy (Basel); 2018 Jul; 20(7):. PubMed ID: 33265599
[TBL] [Abstract][Full Text] [Related]
4. CEBoosting: Online sparse identification of dynamical systems with regime switching by causation entropy boosting.
Chen C; Chen N; Wu JL
Chaos; 2023 Aug; 33(8):. PubMed ID: 37549116
[TBL] [Abstract][Full Text] [Related]
5. Conditional Gaussian nonlinear system: A fast preconditioner and a cheap surrogate model for complex nonlinear systems.
Chen N; Li Y; Liu H
Chaos; 2022 May; 32(5):053122. PubMed ID: 35650001
[TBL] [Abstract][Full Text] [Related]
6. Predictability of extreme events in a nonlinear stochastic-dynamical model.
Franzke C
Phys Rev E Stat Nonlin Soft Matter Phys; 2012 Mar; 85(3 Pt 1):031134. PubMed ID: 22587065
[TBL] [Abstract][Full Text] [Related]
7. An efficient continuous data assimilation algorithm for the Sabra shell model of turbulence.
Chen N; Li Y; Lunasin E
Chaos; 2021 Oct; 31(10):103123. PubMed ID: 34717341
[TBL] [Abstract][Full Text] [Related]
8. Linear theory for filtering nonlinear multiscale systems with model error.
Berry T; Harlim J
Proc Math Phys Eng Sci; 2014 Jul; 470(2167):20140168. PubMed ID: 25002829
[TBL] [Abstract][Full Text] [Related]
9. Can Short and Partial Observations Reduce Model Error and Facilitate Machine Learning Prediction?
Chen N
Entropy (Basel); 2020 Sep; 22(10):. PubMed ID: 33286844
[TBL] [Abstract][Full Text] [Related]
10. A variational approach to probing extreme events in turbulent dynamical systems.
Farazmand M; Sapsis TP
Sci Adv; 2017 Sep; 3(9):e1701533. PubMed ID: 28948226
[TBL] [Abstract][Full Text] [Related]
11. Using machine learning to predict extreme events in complex systems.
Qi D; Majda AJ
Proc Natl Acad Sci U S A; 2020 Jan; 117(1):52-59. PubMed ID: 31871152
[TBL] [Abstract][Full Text] [Related]
12. New perspectives for the prediction and statistical quantification of extreme events in high-dimensional dynamical systems.
Sapsis TP
Philos Trans A Math Phys Eng Sci; 2018 Aug; 376(2127):. PubMed ID: 30037931
[TBL] [Abstract][Full Text] [Related]
13. Short- and long-term predictions of chaotic flows and extreme events: a physics-constrained reservoir computing approach.
Doan NAK; Polifke W; Magri L
Proc Math Phys Eng Sci; 2021 Sep; 477(2253):20210135. PubMed ID: 35153579
[TBL] [Abstract][Full Text] [Related]
14. Beating the curse of dimension with accurate statistics for the Fokker-Planck equation in complex turbulent systems.
Chen N; Majda AJ
Proc Natl Acad Sci U S A; 2017 Dec; 114(49):12864-12869. PubMed ID: 29158403
[TBL] [Abstract][Full Text] [Related]
15. Blended particle filters for large-dimensional chaotic dynamical systems.
Majda AJ; Qi D; Sapsis TP
Proc Natl Acad Sci U S A; 2014 May; 111(21):7511-6. PubMed ID: 24825886
[TBL] [Abstract][Full Text] [Related]
16. Data-assisted reduced-order modeling of extreme events in complex dynamical systems.
Wan ZY; Vlachas P; Koumoutsakos P; Sapsis T
PLoS One; 2018; 13(5):e0197704. PubMed ID: 29795631
[TBL] [Abstract][Full Text] [Related]
17. State estimation and prediction using clustered particle filters.
Lee Y; Majda AJ
Proc Natl Acad Sci U S A; 2016 Dec; 113(51):14609-14614. PubMed ID: 27930332
[TBL] [Abstract][Full Text] [Related]
18. Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control.
Brunton SL; Brunton BW; Proctor JL; Kutz JN
PLoS One; 2016; 11(2):e0150171. PubMed ID: 26919740
[TBL] [Abstract][Full Text] [Related]
19. BAMCAFE: A Bayesian machine learning advanced forecast ensemble method for complex turbulent systems with partial observations.
Chen N; Li Y
Chaos; 2021 Nov; 31(11):113114. PubMed ID: 34881608
[TBL] [Abstract][Full Text] [Related]
20. Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.
Vlachas PR; Byeon W; Wan ZY; Sapsis TP; Koumoutsakos P
Proc Math Phys Eng Sci; 2018 May; 474(2213):20170844. PubMed ID: 29887750
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
