Abstract Submission

6740. An Adaptive ML-Based Discretization Method for Computing Optimal Experimental Designs

Contributed abstract in session FA-5: Optimization and Artificial Intelligence II, stream Optimization and Artificial Intelligence.

Friday, 9:00 - 10:40
Room: Pontryagin

Authors (first author is the speaker)

1. Philipp Seufert
Optimization, Fraunhofer ITWM
2. Jan Schwientek
Optimization, Fraunhofer ITWM
3. Tobias Seidel
Optimization, Fraunhofer Institute ITWM
4. Michael Bortz
Optimization, Fraunhofer ITWM
5. Karl-Heinz K├╝fer
Optimization, Fraunhofer ITWM


Standard algorithms for the computation of optimal experimental designs (OED) consist of an inner point acquisition and an outer weight optimization. Whereas the latter is a convex problem, the inner one is a general non-convex non-linear program with implicitly given objective. We present a modification of the common OED solution approach which uses Bayesian optimization to adaptively form a grid of candidate points for determining the optimal design. We proved convergence of the algorithm to a locally optimal continuous design and obtained promising numerical results on real-world problems.


Status: accepted

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