Numerical Differentiation and Integration

Related lectures (25)

Page 2 of 3

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Covers the Cauchy problem in differential equations, focusing on initial conditions and their impact on solution uniqueness.

Covers NumPy arrays and their graphical representations using Matplotlib, focusing on array creation, manipulation, and visualization techniques.

Covers numerical methods for solving boundary value problems using finite difference, FFT, and finite element methods.

Focuses on Euler schemes for numerical approximation of forces and velocities.

Covers Euler and Crank-Nicolson methods for solving differential equations.

Covers numerical differentiation, integration, finite differences, Taylor expansions, and interpolation polynomials.

Covers numerical methods for root finding, focusing on the secant and Newton's methods.

Covers the motivation and examples of boundary value problems, the finite difference method, and the approximation of local derivatives.

Introduces Real Analysis I, covering real numbers, functions, limits, derivatives, and integrals.