Mixture systems — in which the components of interest are mixed and their proportions are optimized — involve inherent constraints and complex kinetics, making their behavior difficult to predict.

Traditional methods are not up to the challenge of today’s complex needs. They overburden users, assume inadequate models, and require too many experimental runs.

This course presents a revolutionary approach to the design of experiments for mixtures. It is based on modern machine learning methods that reduce sample size requirements, streamline the process of analyzing and modeling, improve accuracy, and provide deep insight for experiments of high complexity.

Applications pertain to a wide variety of industrial sciences. We have had success in:

• Pharmaceutical and bio-engineering, conducting media or buffer optimization experiments for increased protein yields from bacteria or mammalian cells
• Semi-conductors, modeling yield on wafers, identifying yield loss mechanics, and investigating new wafer substrates
• Asphalt, improving asphalt mixtures for critical process attributes
• Metals manufacturing, optimizing critical characteristics in metallurgy

There are applications in almost all industries, including chemical, specialty chemical, pharmaceutical, biotechnology, semiconductor, solar, consumer products, and automotive.

This course will help scientists and engineers of many backgrounds predict and optimize system characteristics in a way that was not possible until now.