159 related articles for article (PubMed ID: 31871152)
1. 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]
2. 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]
3. 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]
4. 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]
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. 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]
7. 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]
8. 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]
9. 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]
10. Cellular automata as convolutional neural networks.
Gilpin W
Phys Rev E; 2019 Sep; 100(3-1):032402. PubMed ID: 31639995
[TBL] [Abstract][Full Text] [Related]
11. Machine learning dynamical phase transitions in complex networks.
Ni Q; Tang M; Liu Y; Lai YC
Phys Rev E; 2019 Nov; 100(5-1):052312. PubMed ID: 31870001
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.
Kim J; Kim J; Jang GJ; Lee M
Neural Netw; 2017 Mar; 87():109-121. PubMed ID: 28110106
[TBL] [Abstract][Full Text] [Related]
14. Statistical and Machine Learning Methods Applied to the Prediction of Different Tropical Rainfall Types.
Wang J; Wong RKW; Jun M; Schumacher C; Saravanan R; Sun C
Environ Res Commun; 2021 Nov; 3(11):. PubMed ID: 35814029
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Using machine learning to predict extreme events in the Hénon map.
Lellep M; Prexl J; Linkmann M; Eckhardt B
Chaos; 2020 Jan; 30(1):013113. PubMed ID: 32013508
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Effective control of complex turbulent dynamical systems through statistical functionals.
Majda AJ; Qi D
Proc Natl Acad Sci U S A; 2017 May; 114(22):5571-5576. PubMed ID: 28507125
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. [Drug discrimination by near infrared spectroscopy based on summation wavelet extreme learning machine].
Liu ZB; Jiang SJ; Yang HH; Zhang XB
Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Oct; 34(10):2815-20. PubMed ID: 25739231
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
