Designing Complex Experiments by Applying Unsupervised Machine Learning

TL;DR

This paper introduces a novel approach using beta-VAE to represent and generate complex experimental designs with multiple factors and constraints, reducing trial numbers while maintaining objectives.

Contribution

It applies unsupervised machine learning, specifically beta-VAE, to efficiently design pragmatic experiments by filtering and representing feasible trials in a low-dimensional latent space.

Findings

Beta-VAE effectively filters unfeasible trials.

Latent space supports interpretable and disentangled representations.

Generated designs can be optimized using clustering and gradient metrics.

Abstract

Design of experiments (DOE) is playing an essential role in learning and improving a variety of objects and processes. The article discusses the application of unsupervised machine learning to support the pragmatic designs of complex experiments. Complex experiments are characterized by having a large number of factors, mixed-level designs, and may be subject to constraints that eliminate some unfeasible trials for various reasons. Having such attributes, it is very challenging to design pragmatic experiments that are economically, operationally, and timely sound. It means a significant decrease in the number of required trials from a full factorial design, while still attempting to achieve the defined objectives. A beta variational autoencoder (beta-VAE) has been applied to represent trials of the initial full factorial design after filtering out unfeasible trials on the low dimensional latent space. Regarding visualization and interpretability, the paper is limited to 2D representations. Beta-VAE supports (1) orthogonality of the latent space dimensions, (2) isotropic multivariate standard normal distribution of the representation on the latent space, (3) disentanglement of the latent space representation by levels of factors, (4) propagation of the applied constraints of the initial design into the latent space, and (5) generation of trials by decoding latent space points. Having an initial design representation on the latent space with such properties, it allows for the generation of pragmatic design of experiments (G-DOE) by specifying the number of trials and their pattern on the latent space, such as square or polar grids. Clustering and aggregated gradient metrics have been shown to guide grid specification.