Fundamental Optimization Methods

An introduction to optimization and the strategic handling of SIMPLEX and BFGS methods for tackling linear and non-linear problems. The module focuses on practical application and the effective solution of common optimization challenges. It is aimed at undergraduate STEM students and professionals seeking a solid foundation in computational optimization.

Evolutionary Optimization and Constraint Handling

Application of stochastic methods inspired by biological processes for seeking global solutions in constrained optimization problems. The module focuses on handling complex design challenges where classical methods often fail. It is aimed at students, researchers, and design engineers targeting optimal solutions in high-dimensional search spaces.

High-Performance Optimization Methods

An introduction to the Adjoint method, which enables the optimization of specific problems with a computational cost decoupled from the number of design variables. This approach focuses on the efficient estimation of the gradient vector with maximum resource economy. It is aimed at PhD researchers and R&D engineers managing high-complexity simulations.