Principles and methods for formulating and analyzing mathematical models of physical systems; Newtonian, Lagrangian, and Hamiltonian formulations of particle and rigid and elastic body dynamics; canonical transformations, Hamilton-Jacobi theory; and integrable and nonintegrable systems. Additional topics are explored at the discretion of the instructor.

This course provides an introduction to the application of deep learning to physical problems. Topics include convolutional neural networks, and graph neural networks.

To develop an understanding of feedback principles in the control of dynamic systems, and to gain experience in analyzing and designing control systems in a laboratory setting.

This course provides an introduction to the application of deep learning to physical problems. Topics include convolutional neural networks, and graph neural networks.