Dynamic Systems in Biology

Related lectures (36)

Covers the formalism and bases of dynamic systems, including differential equations and non-linear systems.

Explores phase portraits in 2D systems and the dynamics of predator-prey models.

Covers open methods, Newton-Raphson, and secant method for iterative solutions in numerical methods.

Covers dynamic systems, trajectories, growth models, stability of fixed points, and linearization of models.

Covers non-linear systems in 2D, focusing on predator-prey models and stability analysis of fixed points.

Introduces numerical methods for solving nonlinear equations, emphasizing the problem position and the Newton method for fixed points.

Covers the convergence of fixed point methods for nonlinear equations, including global and local convergence theorems and the order of convergence.

Explores growth models for populations and gene regulation analysis.

Explores unconfined flow in computational geomechanics, emphasizing weak form derivation and relative permeability.

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

Explores fixed points and their stability in dynamic systems, emphasizing linear stability analysis.

Covers methods for solving nonlinear equations, including bisection and Newton-Raphson methods, with a focus on convergence and error criteria.

Explains how to calculate the orientation of a station in geomatics.

Covers the basics of numerical analysis and computational methods using Python, focusing on algorithms and practical applications in mathematics.

Explores numerical methods in biomechanics for hip implants and emphasizes understanding conditions for improved designs and patient outcomes.

Covers numerical differentiation, forward differences, Taylor's expansion, Big O notation, and error minimization.

Explores the Kuramoto model for synchronization in phase oscillators and discusses stability criteria and critical coupling values.

Explores methods for solving non-linear ODE systems and discusses stability conditions.

Explores advanced space discretization techniques in numerical analysis for solving differential systems efficiently and accurately.

Covers fixed point methods and the Picard method for solving nonlinear equations iteratively.

Page 1 of 2