2021 Virtual Capstone Conference Abstract Detail Page
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| Abstract Title: | Machine Learning in Upper-Level Physics Lab |
|---|---|
| Abstract: | We used machine learning techniques to analyze data in a three-hour, independent-study laboratory course, which was a trial run for an upper-level laboratory course currently in development. Specifically, we compared the effectiveness of a simple classical analysis to that of a machine learning approach to identify structural defects in aluminum sheets, based on the sounds they make when lightly struck. In this presentation I will report on the project and its fit in the physics curriculum. I will also discuss the application of machine learning to physical systems and some of the available resources for non-specialists. |
| Abstract Type: | Contributed Talk |
| Submission Category: | Laboratory/Experimental |
| Session: | Wednesday Contributed Session |
| Presentation: | Download the Presentation |
| Video: | |
| Abstract DOI: | 10.1119/PICUP.Abstract.2021Capstone.8641 |
Primary Author Information | |
| First name: | Peter |
| Last name: | Bryant |
| Institution: | Bethany College |
| Zip Code: | 67456 |
Co-Author Information | |
| Additional Co-authors or Co-presenters: | Nicolas Desch |
