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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.
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.
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Supports system dynamics, agent based and discrete event modeling, allows making hybrid models. ASCEND: Free, GNU General Public License (GPL) C: 2012 For solving small to very large mathematical models, systems of non-linear equations, linear and nonlinear optimisation problems, dynamic systems expressed as differential-algebraic equations.
Balachandran's research spans the fields of applied mathematics, applied physics, mechanics, dynamics, control, and system identification, with an emphasis on the analysis and application of nonlinear phenomena and nonlinear dynamics in diverse systems ranging from rotating machinery, vehicle systems, and space structures to disease dynamics.
A coupled map lattice (CML) is a dynamical system that models the behavior of nonlinear systems (especially partial differential equations).They are predominantly used to qualitatively study the chaotic dynamics of spatially extended systems.
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