Introduction to machine learning in undergraduate physics.
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| Title: | Introduction to machine learning in undergraduate physics. |
|---|---|
| Authors: | Mubin, Shafat1 (AUTHOR) smubin@valdosta.edu, Wellons, Xavier1 (AUTHOR) xswellons@valdosta.edu |
| Source: | American Journal of Physics. Mar2026, Vol. 94 Issue 3, p217-223. 7p. |
| Subjects: | Machine learning, Mathematica (Computer software), Simulation methods & models, Kinematics, Projectiles, Thermodynamics, Physics education |
| Abstract: | We provide an introduction to machine learning methods that can be applied to concepts covered in undergraduate physics, such as projectile motion and experimental force measurements. User-friendly features in Mathematica allow datasets, for instance representing trajectories and microstate configurations, to train the machine learning models, which are then used to predict kinematics parameters or thermodynamic configurations of new input sets. The datasets consist of numerical values and images, which are utilized in the training model with minimal coding. The short and straightforward Mathematica codes, compared to other mainstream programming languages, make the process accessible to users who do not possess extensive programming experience. Editor's Note: You know AI and Machine Learning are important topics to teach your students, but you don't really know how to introduce these tools in your undergraduate physics curriculum? This paper provides you with a few examples, taken from the fields of mechanics and thermodynamics and requiring minimal programming expertise. Using Mathematica's built-in machine learning models, the authors demonstrate how these can be trained on numerical datasets, simulated trajectories, and even pictures to extract physical parameters. For instance, given the 2D trajectory of a projectile, a trained model is able to determine its drag coefficient. These examples illustrate how machine learning can (almost painlessly) be integrated into existing courses, providing students with hands-on experience in modern computational methods while deepening their physics understanding. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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