Title: NeuroBayes Design Optimizer (NBDO) for high-dimensional settings Authors:  Caterina May - Kings College London (United Kingdom)
Kalliopi Mylona - King's College London (United Kingdom)
Davide Pigoli - King's College London (United Kingdom)
Theodoros Ladas - King's College London (United Kingdom) [presenting]
Abstract: Finding an optimal experimental design is computationally challenging, especially in high-dimensional spaces. To tackle this, we introduce the NeuroBayes Design Optimizer (NBDO), which uses neural networks to find optimal designs for high-dimensional models, by reducing the dimensionality of the search space. This approach significantly decreases the computational time needed to find a highly efficient optimal design, as demonstrated in various numerical examples. Comparisons with the Coordinate Exchange (CE) algorithm are presented. The method offers a balance between computational speed and efficiency, laying the groundwork for more reliable design processes.