Related references
Note: Only part of the references are listed.Machine learning for data-driven discovery in solid Earth geoscience
Karianne J. Bergen et al.
SCIENCE (2019)
Attractor reconstruction by machine learning
Zhixin Lu et al.
CHAOS (2018)
Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
Jaideep Pathak et al.
PHYSICAL REVIEW LETTERS (2018)
Randomly distributed embedding making short-term high-dimensional data predictable
Huanfei Ma et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)
Machine learning inverse problem for topological photonics
Laura Pilozzi et al.
COMMUNICATIONS PHYSICS (2018)
Nonlinear effects on Turing patterns: Time oscillations and chaos
J. L. Aragon et al.
PHYSICAL REVIEW E (2012)
Analysis of stationary droplets in a generic Turing reaction-diffusion system
Thomas E. Woolley et al.
PHYSICAL REVIEW E (2010)
Hearing shapes of drums: Mathematical and physical aspects of isospectrality
Olivier Giraud et al.
REVIEWS OF MODERN PHYSICS (2010)
Correlation Between Eigenvalue Spectra and Dynamics of Neural Networks
Qingguo Zhou et al.
NEURAL COMPUTATION (2009)
A fast learning algorithm for deep belief nets
Geoffrey E. Hinton et al.
NEURAL COMPUTATION (2006)
Pattern recognition using asymmetric attractor neural networks
T Jin et al.
PHYSICAL REVIEW E (2005)
Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
H Jaeger et al.
SCIENCE (2004)
General conditions for predictivity in learning theory
T Poggio et al.
NATURE (2004)
Designing asymmetric neural networks with associative memory
H Zhao
PHYSICAL REVIEW E (2004)
Spatial resonances and superposition patterns in a reaction-diffusion model with interacting turing modes
L Yang et al.
PHYSICAL REVIEW LETTERS (2002)
Share Paper
