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Learning on predictions: fusing training and autoregressive inference for long-term spatiotemporal forecasts

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Publication:6650079
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DOI10.1016/j.physd.2024.134371MaRDI QIDQ6650079

Pantelis R. Vlachas, Petros Koumoutsakos

Publication date: 6 December 2024

Published in: Physica D (Search for Journal in Brave)




zbMATH Keywords

LSTMRNNexposure biasautoregressive forecastingBPTT


Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)





Cites Work

  • Time-series learning of latent-space dynamics for reduced-order model closure
  • An artificial neural network framework for reduced order modeling of transient flows
  • Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
  • 10.1162/153244303768966139
  • Machine Learning for Fluid Mechanics
  • Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
  • Machine learning the kinematics of spherical particles in fluid flows
  • Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
  • The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
  • Initializing LSTM internal states via manifold learning




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