Lecture

Nonlinear Dynamics: Stability and Chaos

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In course

PHYS-460: Nonlinear dynamics, chaos and complex systems

The course provides students with the tools to approach the study of nonlinear systems and chaotic dynamics. Emphasis is given to concrete examples and numerical applications are carried out during the exercise sessions. ...

Description

This lecture covers the concepts of fixed point stability, focusing on the behavior of systems near equilibrium points. It discusses the conditions for stability and asymptotic stability, emphasizing the role of Lyapunov functions. The lecture also explores examples of Lotka-Volterra models for competition, illustrating the dynamics of interacting species. Additionally, it delves into the classification of fixed points based on their stability properties, such as centers, nodes, and saddles. The presentation concludes with a discussion on nonlinear dynamics, chaos, and complex systems, highlighting the importance of understanding the behavior of nonlinear systems.

Instructor

Paolo Ricci

Paolo Ricci earned his masters degree in nuclear engineering at the Politecnico di Torino, Turin (Italy) in 2000. His doctoral studies were conducted at the Los Alamos National Laboratory, with focus on kinetic simulation of magnetic reconnection in the Earth's magnetotail. He spent two-and-a-half years as a postdoctoral researcher at Dartmouth College's Department of Physics and Astronomy, where he worked on gyrokinetic simulations of the Z pinch. He joined the EPFL's Swiss Plasma Center (SPC), as a EURATOM fellow in 2006, was named Tenure Track Assistant Professor in June 2010, and Associate Professor in August 2016. He is at the head of the SPC theory group. Paolo Ricci is the recipient of the 2016 Section de Physique Teaching Prize and of the 2021 Craie d'Or award from the EPFL physics bachelor students.

Official source

Ontological neighbourhood

Systems science and engineering

Systems theory: Chaos theory

Related concepts (32)

Lyapunov function

In the theory of ordinary differential equations (ODEs), Lyapunov functions, named after Aleksandr Lyapunov, are scalar functions that may be used to prove the stability of an equilibrium of an ODE. Lyapunov functions (also called Lyapunov’s second method for stability) are important to stability theory of dynamical systems and control theory. A similar concept appears in the theory of general state space Markov chains, usually under the name Foster–Lyapunov functions.

Marginal stability

In the theory of dynamical systems and control theory, a linear time-invariant system is marginally stable if it is neither asymptotically stable nor unstable. Roughly speaking, a system is stable if it always returns to and stays near a particular state (called the steady state), and is unstable if it goes farther and farther away from any state, without being bounded. A marginal system, sometimes referred to as having neutral stability, is between these two types: when displaced, it does not return to near a common steady state, nor does it go away from where it started without limit.

Lyapunov exponent

In mathematics, the Lyapunov exponent or Lyapunov characteristic exponent of a dynamical system is a quantity that characterizes the rate of separation of infinitesimally close trajectories. Quantitatively, two trajectories in phase space with initial separation vector diverge (provided that the divergence can be treated within the linearized approximation) at a rate given by where is the Lyapunov exponent. The rate of separation can be different for different orientations of initial separation vector.

Nonlinear optics

Nonlinear optics (NLO) is the branch of optics that describes the behaviour of light in nonlinear media, that is, media in which the polarization density P responds non-linearly to the electric field E of the light. The non-linearity is typically observed only at very high light intensities (when the electric field of the light is >108 V/m and thus comparable to the atomic electric field of ~1011 V/m) such as those provided by lasers. Above the Schwinger limit, the vacuum itself is expected to become nonlinear.

Complex system

A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication systems, complex software and electronic systems, social and economic organizations (like cities), an ecosystem, a living cell, and ultimately the entire universe.

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