Computational Physics

Catalog description

This course introduces computational methods in physics, focusing on numerical techniques, data analysis, and machine learning. Students will learn to implement algorithms for numerical integration, differential equations, Fourier analysis, and Monte Carlo simulations while exploring applications in classical mechanics, quantum systems, and statistical physics as well as predictive modeling. The integration of data science techniques, such as data visualization and analysis, with machine learning models enables students to tackle predictive and analytical tasks in physics. Practical applications and in-class projects prepare students for research and industry, bridging physics and modern computational tools. Students with a semester level of Freshman 1, Freshman 2 or Sophomore 1 may not enroll.