Rational modeling of multivariate multi-fidelity data

Accurate multi-fidelity modeling is of high importance in the present day engineering design process. It allows to model computationally expensive simulations at a reduced cost by combining simulations with variable fidelity levels. In this paper, a novel algorithm is proposed to build multivariate models from variable fidelity simulations using rational functions. The modeling is based on high-fidelity data and low-fidelity data that is sampled over a parameter space of interest. The former is assumed to be computationally expensive and sparse, whereas the latter is cheaper to obtain but comes at a lower accuracy. It is shown that accurate rational models can be built at a reduced cost by combining these types of data. The effectiveness of the algorithm is applied to several examples and confirmed by numerical results.

Publication year:2016