Lectures related to Reconstruction (sampling theorem) - Examples of Reconstruction

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Explores signal sampling theory, interpolation techniques, and the importance of the sampling theorem in signal processing.

Explores modeling and interpolation techniques in geomatics for accurate surface generation.

Covers the interpolation of nonlinear functions using Lagrange polynomials and error analysis.

Explores convection-diffusion equations, focusing on Poincare constant, interpolation error, singular perturbations, and boundary conditions.

Covers the Finite Element Method, discussing the derivation of the equation of motion and exploring mass and stiffness matrices.

Covers the derivation of the equation of motion, interpolation, Newton's equation, and energy conservation in finite element modeling.