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The non-linear σ-model was introduced by Gell-Mann & Lévy (1960, §6), who named it after a field corresponding to a spinless meson called σ in their model. [1] This article deals primarily with the quantization of the non-linear sigma model; please refer to the base article on the sigma model for general definitions and classical (non ...
In physics, a sigma model is a field theory that describes the field as a point particle confined to move on a fixed manifold. This manifold can be taken to be any Riemannian manifold, although it is most commonly taken to be either a Lie group or a symmetric space.
acados - Open-source framework for (nonlinear) model predictive control providing fast and embedded solvers for nonlinear optimization. (C, MATLAB and Python interface available) μAO-MPC - Open Source Software package that generates tailored code for model predictive controllers on embedded systems in highly portable C code.
It is a generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models. The total least squares approximation of the data is generically equivalent to the best, in the Frobenius norm , low-rank approximation of the data matrix.
The model is a system of three ordinary differential equations now known as the Lorenz equations: ... sigma, rho , beta]: [10, 28, 8 / 3]$ ... for example, in ...
The primary application of the Levenberg–Marquardt algorithm is in the least-squares curve fitting problem: given a set of empirical pairs (,) of independent and dependent variables, find the parameters of the model curve (,) so that the sum of the squares of the deviations () is minimized:
In field theory, skyrmions are homotopically non-trivial classical solutions of a nonlinear sigma model [17] with a non-trivial target manifold topology – hence, they are topological solitons. An example occurs in chiral models [18] of mesons, where the target manifold is a homogeneous space of the structure group
The Volterra series is a model for non-linear behavior similar to the Taylor series.It differs from the Taylor series in its ability to capture "memory" effects. The Taylor series can be used for approximating the response of a nonlinear system to a given input if the output of the system depends strictly on the input at that particular time.