PUB - Publikationen an der Universität Bielefeld

Surrogate models using a suitable orthogonal decomposition and radial basis functions have been proposed by many researchers to reduce the computational complexity of numerical solutions to optimization problems. However, these reduced-order models result in low accuracy, sometimes due to inappropriate initial sampling or the occurrence of optima at vertices. This paper provides an improved intelligent data-driven mechanism for constructing low-dimensional surrogate models using alternative memory-based sampling strategies in an iterative algorithm. Furthermore, the application of surrogate models to optimal control problems is extended. It is shown that surrogate models with Latin hypercube sampling dominate variable-order methods in optimization computation time while maintaining accuracy. They are also shown to be robust to nonlinearities in the model. Therefore, these computationally efficient predictive surrogate models are applicable in various fields, especially for solving inverse problems and optimal control problems, some examples of which are shown in this paper.