DESCRIPTION

Machine learning and artificial intelligence are revolutionizing the analysis and characterization of microstructural images, data, and information in materials science and engineering. Machine learning models are increasingly being used to understand and interpret microstructures, including quantitatively, thereby leveraging predictions of materials properties in the structure-property-processing paradigm.

In this webinar, we will discuss and explore how machine learning has become a valuable tool in the management of microstructural data. This webinar will include talks from leading experts in the field from industry, academia, and government.

AGENDA / DISCUSSION TOPICS

Each talk will be followed by a Q&A session with the speaker.

Talk Presentations:

• Relating Microstructure Features to Response Using Convolutional Neural Networks
  Sean Donegan, Air Force Research Laboratory
• Microstructure informatics: expanding descriptors from molecular to microstructural level
  Olga Wood, University at Buffalo
• Application of Machine Learning to Microstructure Quantification and Understanding
  Ryan Noraas, Pratt & Whitney

   

PARTICIPANTS

Host:

• Elizabeth A. Holm, Carnegie Mellon University

Speakers:

• Olga Wodo, University at Buffalo
• Ryan Noraas, Pratt & Whitney
• Sean Donegan, Air Force Research Laboratory

Webinar/Workshop organized by: MRS

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