Introduction to Computational Science and Engineering

CSE.0002x will teach you how to use to solve problems in engineering and science including simulation of time-dependent phenomena; optimization of systems; and quantification of uncertainty. This course is primarily for learners with some prior programming experience in Python and an introductory knowledge of calculus and mechanics (typical of a first-year college course in these topics). You will spend a considerable amount of time writing programs to implement the concepts covered in the course. For example, you will write programs that will: simulate the descent of a lander in the Martian atmosphere; optimize the placement of cellular towers on the MIT campus; and quantify the likelihood of significant climate temperature rise under different scenarios. Topics covered include: Advanced programming in Python 3 and NumPy Plotting with Matplotlib Initial value problems Discretization with explicit and implicit methods Solution of linear and nonlinear systems of equations Unconstrained optimization and gradient descent Probability, distributions Monte Carlo simulations Confidence intervals

• Python 3, NumPy, and Matplotlib
• Numerical methods to solve initial value problems
• Solution of linear and nonlinear systems of equations
• Unconstrained optimization and gradient descent
• Monte Carlo simulations

• 6.00.1x (Link) Introduction to computer science or equivalent
• Some prior programming experience in Python
• Introductory knowledge of calculus and mechanics (typical of a first-year college course in these topics)