129 related articles for article (PubMed ID: 35719064)
1. 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]
2. 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]
3. Data-driven learning of chaotic dynamical systems using Discrete-Temporal Sobolev Networks.
Kennedy C; Crowdis T; Hu H; Vaidyanathan S; Zhang HK
Neural Netw; 2024 May; 173():106152. PubMed ID: 38359640
[TBL] [Abstract][Full Text] [Related]
4. GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems.
Zhang Z; Shin Y; Em Karniadakis G
Philos Trans A Math Phys Eng Sci; 2022 Aug; 380(2229):20210207. PubMed ID: 35719066
[TBL] [Abstract][Full Text] [Related]
5. Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems.
Sapsis TP; Majda AJ
Proc Natl Acad Sci U S A; 2013 Aug; 110(34):13705-10. PubMed ID: 23918398
[TBL] [Abstract][Full Text] [Related]
6. Generalized neural closure models with interpretability.
Gupta A; Lermusiaux PFJ
Sci Rep; 2023 Jun; 13(1):10634. PubMed ID: 37391424
[TBL] [Abstract][Full Text] [Related]
7. Data-driven prediction in dynamical systems: recent developments.
Ghadami A; Epureanu BI
Philos Trans A Math Phys Eng Sci; 2022 Aug; 380(2229):20210213. PubMed ID: 35719077
[TBL] [Abstract][Full Text] [Related]
8. 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]
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. Prediction of Head Movement in 360-Degree Videos Using Attention Model.
Lee D; Choi M; Lee J
Sensors (Basel); 2021 May; 21(11):. PubMed ID: 34070560
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Reducing echo state network size with controllability matrices.
Whiteaker B; Gerstoft P
Chaos; 2022 Jul; 32(7):073116. PubMed ID: 35907714
[TBL] [Abstract][Full Text] [Related]
13. Data-driven nonlinear model reduction to spectral submanifolds in mechanical systems.
Cenedese M; Axås J; Yang H; Eriten M; Haller G
Philos Trans A Math Phys Eng Sci; 2022 Aug; 380(2229):20210194. PubMed ID: 35719078
[TBL] [Abstract][Full Text] [Related]
14. Echo state network for two-dimensional turbulent moist Rayleigh-Bénard convection.
Heyder F; Schumacher J
Phys Rev E; 2021 May; 103(5-1):053107. PubMed ID: 34134328
[TBL] [Abstract][Full Text] [Related]
15. Learning epidemic threshold in complex networks by Convolutional Neural Network.
Ni Q; Kang J; Tang M; Liu Y; Zou Y
Chaos; 2019 Nov; 29(11):113106. PubMed ID: 31779342
[TBL] [Abstract][Full Text] [Related]
16. Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning.
Bouhamed O; Amayri M; Bouguila N
Sensors (Basel); 2022 Apr; 22(9):. PubMed ID: 35590880
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. 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]
20. Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator.
Gallos IK; Lehmberg D; Dietrich F; Siettos C
Chaos; 2024 Jan; 34(1):. PubMed ID: 38285718
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]