Using model order reduction for the parameter optimization of large scale dynamical systems

Optimization problems such as the parameter design of dynamical systems are often computationally expensive. In this paper, we apply Krylov based model order reduction techniques to the parameter design problem of an acoustic cavity to accelerate the computation of both function values and derivatives, and therefore, drastically improve the performance of the optimization algorithms. Two types of model reduction techniques are explored: conventional model reduction and parameterized model reduction. The moment matching properties of derivative computation via the reduced model are discussed. Numerical results show that both methods are efficient in reducing the optimization time.edition: 1status: publishe