Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate,regime transitions, and the effect of stochasticity
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Publication:3388699
DOI10.1063/5.0042598zbMath1459.86011OpenAlexW3136433498MaRDI QIDQ3388699
Michelle Girvan, Daniel Canaday, Edward Ott, Dhruvit Patel, Andrew Pomerance
Publication date: 6 May 2021
Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1063/5.0042598
Computational methods for problems pertaining to geophysics (86-08) Climate science and climate modeling (86A08)
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- Reservoir computing approaches to recurrent neural network training
- Stochastic climate dynamics: random attractors and time-dependent invariant measures
- The theory of parallel climate realizations. A new framework of ensemble methods in a changing climate: an overview
- Extreme sensitivity and climate tipping points
- Statistical interpretation of the interplay between noise and chaos in the stochastic logistic map
- Dimension formula for random transformations
- Quantitative universality for a class of nonlinear transformations
- Robustness of LSTM neural networks for multi-step forecasting of chaotic time series
- Regime shifts in models of dryland vegetation
- Tipping elements in the Earth's climate system
- Predicting critical transitions in multiscale dynamical systems using reservoir computing
- Spatiotemporal chaos in terms of unstable recurrent patterns
- On Flame Propagation Under Conditions of Stoichiometry
- Calculation of the Wasserstein Distance Between Probability Distributions on the Line
- Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
- Transition to chaos for random dynamical systems
- Ergodic theory of chaos and strange attractors
- Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
- Deterministic Nonperiodic Flow
- Chaos in Dynamical Systems
- Crises, sudden changes in chaotic attractors, and transient chaos
- Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos
- On Information and Sufficiency
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