This paper reviews the major developments of modeling techniques applied to nonlinear
dynamics and chaos. Model representations, parameter estimation techniques, data …

Chaotic dynamical systems are ubiquitous in nature and most of them does not have an
explicit dynamical equation and can be only understood through the available time series …

We present an improved and enlarged version of our book Nonlinear-namics of Chaotic and
Stochastic Systems published by Springer in 2002. Basically, the new edition of the book …

Monitoring of water resources and a better understanding of the eco-hydrological processes
governing their dynamics are necessary to anticipate and develop measures to adapt to …

In the analysis of a scalar time series, which lies on an m-dimensional object, a great
number of techniques will start by embedding such a time series in ad-dimensional space …

Although recent advances in oncology emphasized the role of microenvironment in tumor
growth, the role of delays for modeling tumor growth is still uncertain. In this paper, we …

A data-driven approach insensitive to the initial conditions was developed to extract
governing equations for the concentration of CO2 in the Altamira cave (Spain) and its two …

A great number of techniques developed for studying nonlinear dynamical systems start with
the embedding of a scalar time series, lying on an m-dimensional object, in an embedding …

We present TSR-VFD, a novel deep learning solution that recovers temporal super-
resolution (TSR) of three-dimensional vector field data (VFD) for unsteady flow. In scientific …

While predicting the course of an epidemic is difficult, predicting the course of a pandemic
from an emerging virus is even more so. The validity of most predictive models relies on …