Lecture

Finite Element Method: Isoparametric Formulation

Related lectures (31)

Covers the importance of reducing integration order for more efficient calculations.

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

Covers the formulation of isoparametric elements and integration techniques in the finite element method, with a focus on Gauss integration.

Introduces the basics of finite element modeling for dynamics and discusses the Newmark method for time integration.

Covers the basics of the Finite Element Method for solid and structural modeling.

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

Covers finite element methods for solving diffusion problems in porous media, including meshing, interpolation, and weighted residuals.

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

Covers the basics of numerical analysis, focusing on interpolation methods and their applications in engineering.

Covers Lagrange interpolation using intervals to find accurate polynomial approximations.

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

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

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

Explains the iso-parametric formulation of the quad4 planar element and the calculation of the stiffness matrix.

Explains the construction of basis functions in the Finite Element Method, focusing on local to global mapping and numerical accuracy.

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

Covers the fundamentals of finite element analysis, including traction and interpolation functions.

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

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

Covers the isoparametric formulation of the Quad4 planar element and stiffness matrix calculation.