Greedy Kernel Approximation for Sparse Surrogate Modeling

Bernard Haasdonk () and Gabriele Santin ()
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Bernard Haasdonk: University of Stuttgart, Institute of Applied Analysis and Numerical Simulation
Gabriele Santin: University of Stuttgart, Institute of Applied Analysis and Numerical Simulation

A chapter in Reduced-Order Modeling (ROM) for Simulation and Optimization, 2018, pp 21-45 from Springer

Abstract: Abstract Modern simulation scenarios frequently require multi-query or real-time responses of simulation models for statistical analysis, optimization, or process control. However, the underlying simulation models may be very time-consuming rendering the simulation task difficult or infeasible. This motivates the need for rapidly computable surrogate models. We address the case of surrogate modeling of functions from vectorial input to vectorial output spaces. These appear, for instance, in simulation of coupled models or in the case of approximating general input–output maps. We review some recent methods and theoretical results in the field of greedy kernel approximation schemes. In particular, we recall the vectorial kernel orthogonal greedy algorithm (VKOGA) for approximating vector-valued functions. We collect some recent convergence statements that provide sound foundation for these algorithms, in particular quasi-optimal convergence rates in case of kernels inducing Sobolev spaces. We provide some initial experiments that can be obtained with non-symmetric greedy kernel approximation schemes. The results indicate better stability and overall more accurate models in situations where the input data locations are not equally distributed.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-75319-5_2

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DOI: 10.1007/978-3-319-75319-5_2

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