A Tri-Level Approach for T-Criterion-Based Model Discrimination

David Mogalle, Philipp Seufert, Jan Schwientek (), Michael Bortz and Karl-Heinz Küfer
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David Mogalle: Fraunhofer ITWM
Philipp Seufert: Fraunhofer ITWM
Jan Schwientek: Fraunhofer ITWM
Michael Bortz: Fraunhofer ITWM
Karl-Heinz Küfer: Fraunhofer ITWM

Chapter Chapter 11 in Operations Research Proceedings 2022, 2023, pp 87-93 from Springer

Abstract: Abstract Model discrimination (MD) aims to determine the inputs, called design points, of two or more models at which these models differ most under the additional condition that the models are fitted to these points, in the case of T-optimal designs. On the one hand, nonlinear models often lead to nonconvex MD problems, on the other hand, the optimal number of design points must be determined, too. Thus, the computation of T-optimal designs is very arduous. However, if one considers finitely many design points, a well-solvable bi-level problem arises. Since the latter only represents an approximation of the original model discrimination problem, we refine the design space discretization using the equivalence theorem of MD. This yields a tri-level approach whose iterates converge to a T-optimal design. We demonstrate that the approach can outperform known solution methods on an example from chemical process engineering.

Keywords: Model discrimination; Multi-level optimization; Discretization (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-24907-5_11

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DOI: 10.1007/978-3-031-24907-5_11

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