In this online webinar, Cambridge machine learning specialist teams up with NASA Glenn Research Center to show how machine learning approaches are being validated and applied to the design of materials and components for high performance engineering applications.
Webinar with NASA: Machine learning for material and component design
Find out about the Alchemite™ deep learning software for design of materials and components, including a case study presentation from our guest speaker, Dr Josh Stuckner, Materials Informatics Scientist at NASA Glenn Research Center. Development organisations in sectors such as aerospace, automotive, and materials production need to design new and improved materials and components and to wring every drop of performance from existing systems. To do this, they want to learn from all available data in order to optimise properties while minimising the amount of expensive testing and simulation required to generate results. Machine learning can support these objectives but often runs into practical problems, because the available data is sparse and noisy, because methods are time-consuming to set up and run, or because scientists are unsure how much confidence to place in results. In this webinar, we’ll introduce and demonstrate Alchemite™ - a novel method that solves these problems. And we’ll hear about a project to test and validate Alchemite™ at NASA Glenn Research Center.
Intellegens helps clients to optimise products and processes and accelerate innovation through unique deep learning software to extract valuable information from existing processes and data. Our primary focus is on materials, chemicals, drug discovery, and manufacturing applications.