Nonlinear Dynamical Systems with Chaos and Big Data: A Case Study of Epileptic Seizure Prediction and Control

2017 ◽  
pp. 329-369 ◽  
Author(s):  
Ashfaque Shafique ◽  
Mohamed Sayeed ◽  
Konstantinos Tsakalis
Keyword(s):  
Big Data ◽  
Dynamical Systems ◽  
Epileptic Seizure ◽  
Nonlinear Dynamical Systems ◽  
Seizure Prediction ◽  
Nonlinear Dynamical ◽  
Prediction And Control ◽  
Epileptic Seizure Prediction ◽  
And Control

scholarly journals Parsimonious representation of nonlinear dynamical systems through manifold learning: A chemotaxis case study

2018 ◽  
Vol 44 (3) ◽  
pp. 759-773 ◽  
Author(s):  
Carmeline J. Dsilva ◽  
Ronen Talmon ◽  
Ronald R. Coifman ◽  
Ioannis G. Kevrekidis
Keyword(s):  
Dynamical Systems ◽  
Manifold Learning ◽  
Nonlinear Dynamical Systems ◽  
Nonlinear Dynamical

scholarly journals Artificial Intelligence, Chaos, Prediction and Understanding in Science

2021 ◽  
Vol 31 (11) ◽  
pp. 2150173
Author(s):  
Miguel A. F. Sanjuán
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Dynamical Systems ◽  
Chaotic Dynamics ◽  
Nonlinear Dynamical Systems ◽  
Nonlinear Dynamical ◽  
Constructive Dialogue ◽  
Learning Techniques

Machine learning and deep learning techniques are contributing much to the advancement of science. Their powerful predictive capabilities appear in numerous disciplines, including chaotic dynamics, but they miss understanding. The main thesis here is that prediction and understanding are two very different and important ideas that should guide us to follow the progress of science. Furthermore, the important role played by nonlinear dynamical systems is emphasized for the process of understanding. The path of the future of science will be marked by a constructive dialogue between big data and big theory, without which we cannot understand.

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