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Nonlinear Dynamics, An International Journal of Nonlinear Dynamics and Chaos in Engineering Systems is a monthly peer-reviewed scientific journal covering all nonlinear dynamic phenomena associated with mechanical, structural, civil, aeronautical, ocean, electrical, and control systems.
The Journal of Computational and Nonlinear Dynamics is a quarterly peer-reviewed multidisciplinary scientific journal covering the study of nonlinear dynamics. It was established in 2006 and is published by the American Society of Mechanical Engineers. The editor-in-chief is Balakumar Balachandran (University of Maryland).
In mathematics and science, a nonlinear system (or a non-linear system) is a system in which the change of the output is not proportional to the change of the input. [1] [2] Nonlinear problems are of interest to engineers, biologists, [3] [4] [5] physicists, [6] [7] mathematicians, and many other scientists since most systems are inherently nonlinear in nature. [8]
Sparse identification of nonlinear dynamics (SINDy) is a data-driven algorithm for obtaining dynamical systems from data. [1] Given a series of snapshots of a dynamical system and its corresponding time derivatives, SINDy performs a sparsity-promoting regression (such as LASSO) on a library of nonlinear candidate functions of the snapshots against the derivatives to find the governing equations.
In the past few decades, chaos and nonlinear dynamics have been used in the design of hundreds of cryptographic primitives. These algorithms include image encryption algorithms, hash functions, secure pseudo-random number generators, stream ciphers, watermarking, and steganography. [123]
Chaos: An Interdisciplinary Journal of Nonlinear Science is a monthly peer-reviewed scientific journal covering nonlinear systems and describing their manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
However, real-world systems are often nonlinear and multidimensional, in some instances rendering explicit equation-based modeling problematic. Empirical models, which infer patterns and associations from the data instead of using hypothesized equations, represent a natural and flexible framework for modeling complex dynamics.
One example of nonlinear filters is the (generalized directional distance rational hybrid filter (GDDRHF) [1]) for multidimensional signal processing.This filter is a two-stage type hybrid filter: 1) the stage norm criteria and angular distance criteria to produce three output vectors with respect to the shape models; 2) the stage performs vector rational operation on the above three output ...