Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos

DOI10.1063/1.5124926zbMath1429.37047OpenAlexW2996325614WikidataQ92362465 ScholiaQ92362465MaRDI QIDQ5213524

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Publication date: 3 February 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1063/1.5124926

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Time series analysis of dynamical systems (37M10)

Related Items (2)

Reinforcement learning for suppression of collective activity in oscillatory ensembles ⋮ Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate,regime transitions, and the effect of stochasticity

Uses Software

• darch
• Python
• TensorFlow
• Keras
• ImageNet
• AlexNet

Cites Work

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• Accurate state and parameter estimation in nonlinear systems with sparse observations
• Exponential time differencing for stiff systems
• Predicting spatio-temporal time series using dimension reduced local states
• Reducing the Dimensionality of Data with Neural Networks
• An Introduction to Conditional Random Fields
• Automatic Selection of Methods for Solving Stiff and Nonstiff Systems of Ordinary Differential Equations
• Nonlinear analysis of hydrodynamic instability in laminar flames—I. Derivation of basic equations
• On Flame Propagation Under Conditions of Stoichiometry
• Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
• Deterministic Nonperiodic Flow

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