Optimization
Summary
In optimization, the main goal is to find the maximum or minimum of an objective function given some constraints and bounds for the input variables. In regards to engineering, the goal may be to minimize the weight of a structure given the constraint that the structure does not fail and is within the design limits.
Since carrying out optimization of engineering problems is computationally intensive, our group focuses on using the surrogate models such as Kriging, Radial basis function, polynomial chaos expansion, artificial neural networks, etc. for the objective function and constraints during optimization.
Once the surrogate models are built, the optimization can be run using either gradient-based solver such sequential quadratic programming or heuristic approaches such as genetic algorithm or particle swarm optimization.
Since carrying out optimization of engineering problems is computationally intensive, our group focuses on using the surrogate models such as Kriging, Radial basis function, polynomial chaos expansion, artificial neural networks, etc. for the objective function and constraints during optimization.
Once the surrogate models are built, the optimization can be run using either gradient-based solver such sequential quadratic programming or heuristic approaches such as genetic algorithm or particle swarm optimization.
Surrogate-based optimization using Kriging
One of the examples of application of optimization was to optimize the curvilinear stiffened panel for the aircraft skin and fuselage.