ENBIS-23 Valencia Conference

Session

CONTRIBUTED Special Session: Design of Experiments

12 Sept 2023, 16:40

2.7/2.8

Conveners

CONTRIBUTED Special Session: Design of Experiments

• Jeroen de Mast (University of Waterloo + JADS)

78. Broadening the Spectrum of OMARS Designs

Prof. Peter Goos (University of Leuven), Dr José Núñez Ares (University of Leuven)

12/09/2023, 16:40

Design and analysis of experiments

The family of orthogonal minimally aliased response surface designs or OMARS designs bridges the gap between the small definitive screening designs and classical response surface designs. The initial OMARS designs involve three levels per factor and allow large numbers of quantitative factors to be studied efficiently. Many of the OMARS designs possess good projection properties and offer...

79. Self-Validated Ensemble Models (SVEM) – Machine Learning for Small Data Typical of Industrial Designed Experiments

Dr Christopher Gotwalt (JMP Statistical Discovery LLC)

12/09/2023, 17:00

Machine learning

Self-Validating Ensemble Modeling (S-VEM) is an exciting, new approach that combines machine learning model ensembling methods to Design of Experiments (DOE) and has many applications in manufacturing and chemical processes. In most applications, practitioners avoid machine learning methods with designed experiments because often one cannot afford to hold out runs for a validation set without...

80. Multi-Objective Optimisation Under Uncertainty

Dr Semochkina Dasha (Southampton Statistical Sciences Research Institute (S3RI))

12/09/2023, 17:20

Broadly speaking, Bayesian optimisation methods for a single objective function (without constraints) proceed by (i) assuming a prior for the unknown function f (ii) selecting new points x at which to evaluate f according to some infill criterion that maximises an acquisition function; and (iii) updating an estimate of the function optimum, and its location, using the updated posterior for f....