Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Covers unconstrained and constrained optimization, optimal control, neural networks, and global optimization methods.
Covers advanced numerical analysis topics including deep neural networks and optimization methods.
Covers the Runge Kutta method and its application to optimal control and neural networks.
Covers optimization methods, focusing on gradient methods and line search techniques.
Covers line search, Newton's method, BFGS, and conjugate gradient in nonlinear optimization.
Covers the derivation of the equation of motion, interpolation, Newton's equation, and energy conservation in finite element modeling.
Covers the Finite Element Method, discussing the derivation of the equation of motion and exploring mass and stiffness matrices.
Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.
