122 related articles for article (PubMed ID: 38920531)
1. Unambiguous Models and Machine Learning Strategies for Anomalous Extreme Events in Turbulent Dynamical System.
Qi D
Entropy (Basel); 2024 Jun; 26(6):. PubMed ID: 38920531
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Machine learning-based statistical closure models for turbulent dynamical systems.
Qi D; Harlim J
Philos Trans A Math Phys Eng Sci; 2022 Aug; 380(2229):20210205. PubMed ID: 35719064
[TBL] [Abstract][Full Text] [Related]
4. A random batch method for efficient ensemble forecasts of multiscale turbulent systems.
Qi D; Liu JG
Chaos; 2023 Feb; 33(2):023113. PubMed ID: 36859236
[TBL] [Abstract][Full Text] [Related]
5. Linear and nonlinear statistical response theories with prototype applications to sensitivity analysis and statistical control of complex turbulent dynamical systems.
Majda AJ; Qi D
Chaos; 2019 Oct; 29(10):103131. PubMed ID: 31675803
[TBL] [Abstract][Full Text] [Related]
6. Extreme learning machine for reduced order modeling of turbulent geophysical flows.
San O; Maulik R
Phys Rev E; 2018 Apr; 97(4-1):042322. PubMed ID: 29758628
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. Model-assisted deep learning of rare extreme events from partial observations.
Asch A; J Brady E; Gallardo H; Hood J; Chu B; Farazmand M
Chaos; 2022 Apr; 32(4):043112. PubMed ID: 35489849
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. High-order moment closure models with random batch method for efficient computation of multiscale turbulent systems.
Qi D; Liu JG
Chaos; 2023 Oct; 33(10):. PubMed ID: 37871000
[TBL] [Abstract][Full Text] [Related]
12. Inferring turbulent environments via machine learning.
Buzzicotti M; Bonaccorso F
Eur Phys J E Soft Matter; 2022 Dec; 45(12):102. PubMed ID: 36586035
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.
Woldaregay AZ; Årsand E; Walderhaug S; Albers D; Mamykina L; Botsis T; Hartvigsen G
Artif Intell Med; 2019 Jul; 98():109-134. PubMed ID: 31383477
[TBL] [Abstract][Full Text] [Related]
15. Training a neural network to predict dynamics it has never seen.
Pershin A; Beaume C; Li K; Tobias SM
Phys Rev E; 2023 Jan; 107(1-1):014304. PubMed ID: 36797895
[TBL] [Abstract][Full Text] [Related]
16. Modeling and inference methods for switching regime-dependent dynamical systems with multiscale neural observations.
Song CY; Hsieh HL; Pesaran B; Shanechi MM
J Neural Eng; 2022 Nov; 19(6):. PubMed ID: 36261030
[No Abstract] [Full Text] [Related]
17. 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]
18. 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]
19. Statistical dynamical model to predict extreme events and anomalous features in shallow water waves with abrupt depth change.
Majda AJ; Moore MNJ; Qi D
Proc Natl Acad Sci U S A; 2019 Mar; 116(10):3982-3987. PubMed ID: 30760588
[TBL] [Abstract][Full Text] [Related]
20. Conceptual dynamical models for turbulence.
Majda AJ; Lee Y
Proc Natl Acad Sci U S A; 2014 May; 111(18):6548-53. PubMed ID: 24753605
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
