Speaker
Theodoros Ladas
(King's College London)
Description
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. The method offers a balance between computational speed and efficiency, laying the groundwork for more reliable design processes.
| Type of presentation | Talk |
|---|---|
| Classification | Mainly methodology |
| Keywords | Design of experiments, High dimensional data, neural network algorithm |
Primary author
Theodoros Ladas
(King's College London)
Co-authors
Dr
Davide Pigoli
(King's College London)
Dr
Kalliopi Mylona
(King's College London)