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VIEW ALL COURSE TIMES AND SESSIONSPrerequisites
Linear algebra, calculus of multivariable, and basic concepts on experimentation
Description
This course provides an in-depth introduction to the application of machine learning (ML) methods in experimental solid mechanics, bridging the gap between traditional mechanics and modern data-driven approaches. Through a combination of lectures, discussions, and hands-on programming assignments, students will explore core ML techniques—including neural networks (NN), advanced neural network architectures, neural operators, and Bayesian inference—and their role in analyzing and interpreting experimental data. Each week, we will examine real-world applications of deep learning in experimental mechanics, investigating how ML models can extract insights, enhance data processing, and improve predictive capabilities. Students will develop practical skills in Python, implementing ML algorithms and leveraging datasets from GitHub to replicate and analyze results from contemporary research literature. By the end of the course, students will have built a solid foundation in ML techniques relevant to experimental solid mechanics, enabling them to apply these powerful tools to their own research and engineering challenges.
