These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

164 related articles for article (PubMed ID: 29795631)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.
    Zhang X; Zhang Q; Zhang G; Nie Z; Gui Z; Que H
    Int J Environ Res Public Health; 2018 May; 15(5):. PubMed ID: 29883381
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. [Dynamic paradigm in psychopathology: "chaos theory", from physics to psychiatry].
    Pezard L; Nandrino JL
    Encephale; 2001; 27(3):260-8. PubMed ID: 11488256
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Nonlinear model reduction for dynamical systems using sparse sensor locations from learned libraries.
    Sargsyan S; Brunton SL; Kutz JN
    Phys Rev E Stat Nonlin Soft Matter Phys; 2015 Sep; 92(3):033304. PubMed ID: 26465583
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. LSTM Model for Prediction of Heart Failure in Big Data.
    Maragatham G; Devi S
    J Med Syst; 2019 Mar; 43(5):111. PubMed ID: 30888519
    [TBL] [Abstract][Full Text] [Related]  

  • 13. LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors.
    Zarzycki K; Ławryńczuk M
    Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34451065
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Data-driven discovery of coordinates and governing equations.
    Champion K; Lusch B; Kutz JN; Brunton SL
    Proc Natl Acad Sci U S A; 2019 Nov; 116(45):22445-22451. PubMed ID: 31636218
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Nonintrusive reduced order modeling framework for quasigeostrophic turbulence.
    Rahman SM; Pawar S; San O; Rasheed A; Iliescu T
    Phys Rev E; 2019 Nov; 100(5-1):053306. PubMed ID: 31869971
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Deep learning for centre manifold reduction and stability analysis in nonlinear systems.
    Ghadami A; Epureanu BI
    Philos Trans A Math Phys Eng Sci; 2022 Aug; 380(2229):20210212. PubMed ID: 35719074
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Modeling of dynamical systems through deep learning.
    Rajendra P; Brahmajirao V
    Biophys Rev; 2020 Nov; 12(6):1311-20. PubMed ID: 33222032
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Physics-incorporated convolutional recurrent neural networks for source identification and forecasting of dynamical systems.
    Saha P; Dash S; Mukhopadhyay S
    Neural Netw; 2021 Dec; 144():359-371. PubMed ID: 34547672
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.