Simplex-based adaptive parametric model order reduction for applications in optimization

Mateus A. O. Leite , Benoît Delinchant (1, 2) , Jean-Michel Guichon (1, 2) , João A. Vasconcelos (3, 2) , Jean‐michel Guichon

1 G2ELab - Laboratoire de Génie Electrique de Grenoble
2 LIA817 - LIA franco brésilien James Clerk Maxwell (CNRS-CNPq)
3 DEE - UFMG - Departamento de Engenharia Elétrica [Minas Gerais]

A new methodology for optimization using parametric reduced order models is introduced. An adaptive scheme to place the expansion points in the reduction step is combined with a smart parametric space sampling in the optimization step to produce very reliable reduced order models. The methodology is illustrated in a demonstrative electromagnetic problem with appreciable gain in computational time. This technique is of general application and can be used to speed up optimization processes in many areas of engineering.

Mots clés: moment matching; parametric model order reduction; dynamic systems

Domaines: Energie électrique

Mateus A. O. Leite, Benoît Delinchant, Jean-Michel Guichon, João A. Vasconcelos, Jean‐michel Guichon. Simplex-based adaptive parametric model order reduction for applications in optimization. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2019, 32 (4), ⟨10.1002/jnm.2264⟩. ⟨hal-01692439⟩