Lecture

Electromagnetic Problems: Well-Posedness and Convergence

Related lectures (32)

Explores trigonometric interpolation for approximating periodic functions and signals using equally spaced nodes.

Covers the Newton method for finding zeros of functions using data interpolation.

Covers Lagrange interpolation using intervals to find accurate polynomial approximations.

Explores interpolation of regular functions, error analysis, convergence, and Chebyshev polynomials.

Covers the role of symmetries and groups in quantum mechanics, focusing on SU2 and SU3, their properties, and implications for physical theories.

Covers the concept of rigid displacement and potential energy, emphasizing continuity between elements and the importance of conforming to the mesh.

Explores constructing a piecewise continuous function using degree 2 interpolation by intervals.

Covers the least squares method for approximating data and handling errors.

Explores convolution operators, interpolation spaces, and function convergence in different spaces.

Explores fundamental groups, homotopy classes, and coverings in connected manifolds.

Explores error analysis and limitations in interpolation on evenly distributed nodes.

Covers the description of problem solutions and the concept of compactness and uniform continuity.

Explores the interpolation of a continuous function by a polynomial.

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

Covers weak derivatives, interpolation spaces, and continuity in function spaces.

Covers the convergence theorems for interpolation and the application of polynomials for equispaced points.

Explores Gauss-Legendre quadrature formulas using Legendre polynomials for accurate function approximation.

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.

Covers advanced numerical analysis topics including deep neural networks and optimization methods.