Pre-Conference Course
Machine Learning for Mechanics
DATE/TIME:Sunday, June 1, 2025 | 9:00 a.m. - 6:00 p.m.
DESCRIPTION:
This course will introduce the basics of machine learning (ML) with an emphasis on its use in mechanics and related material science applications. The course will cover terminology, ML objectives/tasks, common ML algorithms, examples of ML, and approaches to evaluating ML-related work. The course will also offer hands-on exercises using ML on real data.
COURSE OUTLINE:
- Introduction to Machine Learning:
- Types of ML
- Terminology
- ML Tasks in Mechanics
- Overview of Different ML Algorithms and Common Uses o Basic Examples of ML on Data
- Evaluation of ML in Mechanics (become a better reviewer and user of ML in mechanics)
- Examples of ML in Mechanics
- Skill Building Exercises using student-supplied laptops
INSTRUCTORS:
Dr. Sharlotte Kramer is a Distinguished Member of Technical Staff at Sandia National Laboratories, where she has worked for since 2011. She leads a research program in the Engineering Science Center that advances predictive solid mechanics for rapid transformation of qualification...
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Marco Rossi is an Associate Professor at Università Politecnica delle Marche (Italy), where he co-leads the Impact and Material Mechanics Lab. He earned his PhD from the same university and pursued postdoctoral research at ENSAM ParisTech and MIT before joining the faculty...
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I am an assistant professor in Materials Science and Engineering at UIUC. I hold a Ph.D. in Solid Mechanics, Materials Science, and Mechanical Engineering. In 2012, I joined the research group of T.M. Pollock at the University of California Santa Barbara (UCSB), where I became a Specialist...
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Neal Brodnik is currently a postdoctoral scholar at the University of California, Santa Barbara. Prior to that, he got his PhD at Caltech in Materials Science, focusing on full-field material mechanics. His current work uses machine learning methods for materials microstructure generation...
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Bio coming soon.
COURSE FEE
The regular course fee is $500 and the student fee is $250. Course fee includes lunch, course handout material, and refreshment breaks. Lodging and additional food or materials are not included. All course registrants must pay the applicable course fee.
CANCELLATION LIABILITY
If the course is cancelled for any reason, the Society for Experimental Mechanics’ liability is limited to the return of the course fees.
Attendees should bring their own laptops.