Lectures related to Chaos Theory: Discrete Dynamical Systems

Explores chaotic maps, fix points, stability, and Lyapunov exponents in discrete systems, emphasizing their role in determining chaos.

Explores Chaos Theory, focusing on the logistic map, periodic orbits, and stability conditions.

Delves into Chaos Theory, exploring chaotic systems, sensitivity to initial conditions, and the dynamics of the double pendulum.

Covers the introduction to Quantum Chaos, classical chaos, sensitivity to initial conditions, ergodicity, and Lyapunov exponents.

Explores chaotic maps, fix points, periodic orbits, and intermittent chaos.

Explores the Lorenz System, sensitivity to initial conditions, chaotic systems, and topological mixing.

Covers chaotic behavior in complex systems, with applications in various fields and a historical overview of major developments in chaos theory.

Explores chaos, irregular trajectories, and sensitivity to initial conditions in dynamical systems.

Covers Lyapunov exponents, chaos measurement, and perturbation analysis in dynamical systems.

Introduces dynamical systems, equilibrium points, stability, vector fields, phase plots, and Lyapunov stability.

Explores one-dimensional maps, periodic solutions, and bifurcations in dynamical systems.

By Yakov Pesin delves into the essential coexistence phenomenon in Hamiltonian dynamics, exploring types I and II and providing examples and proofs.

Covers dynamical systems, equilibrium points, stability analysis, and phase plots using examples like the pendulum system.

Explores chaotic maps, fractal dimensions, and strange attractors in dynamical systems.

Explores chaotic systems, fractals, and dimensions in Chaos Theory.

Explores elements from Ergodic Theory, transformations, invariant sets, and Lyapunov Exponents for 1-dimensional maps.

Explores Stable Estimator of Dynamical Systems (SEDS) for robots, covering stability, modeling, optimization, and limitations.

Explores the exponential mixing of 3D Anosov flows and their geometric properties, including chaotic behavior and hyperbolic dynamics.

Presents a short proof of a conjecture by Erdös, exploring related questions and detailed proof of the proposition.

Explores learning and adaptive control for robots through SEDS and LPV-DS, emphasizing stability, non-linear dynamics, and optimization.