JMP^®: Design of Experiments for Mixtures Using Machine Learning

When working with mixture systems, engineers and scientists want to know how components of interest are mixed and how their proportions are optimized.

Unfortunately, they often find it challenging to make such predictions, due to inherent constraints and complex kinetics.

This course is based on modern machine learning methods that:

• Reduce sample size requirements
• Streamline the process of analyzing and modeling
• Improve accuracy
• Provide deep insight for experiments of high complexity

Applications of this course pertain to a wide variety of industrial sciences, such as:

• 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

This course will help your scientists and engineers of many backgrounds predict and optimize mixture system characteristics.

Duration: 2 half-day sessions