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Build Better and Faster with Small Experimental Designs and Machine Learning
This webinar centers on Self-Validating Ensemble Modeling (SVEM), a new modeling technique to support these goals, resulting in a greater ability to advance products rapidly from conception to maturity in shorter timeframes.

Discover how you can:
· Use SVEM, an innovative machine learning technique, to model small data sets.
· Achieve product and process development improvements in a fraction of the typical number of experimental runs using SVEM together with Design of Experiments (DOE).
· Construct accurate and stable predictive models by combining SVEM with space-filling designs, which are designs that spread design points over the space of interest.
· Find out why traditional linear models and classical designed experiments are often inadequate for modeling today’s highly complex products and processes.
· Leverage the proven, field-tested SVEM methodology by seeing how it has resulted in improvements in two real-world scenarios—pharmaceuticals and metallurgy.
· Find out how SVEM has been validated in terms of prediction performance in journal articles.

Join our panel of experts for a closer look at how using SVEM enhances your experimentation workflow to fuel innovation in product and process development.

Who should attend:
Engineers, scientists, project managers and anyone else who works in research, or process or product development

Presenters:
Phil Ramsey
Marie Gaudard
Wayne Levin

Jun 20, 2022 11:00 AM in Eastern Time (US and Canada)

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