Scientific Computing for Engineers

3 credits · Graduate Programs

Basic algorithms for scientific computing: Linear systems; eigenvalue problems; curve fitting; numerical differentiation and integration. Differential equations: initial and boundary value problems; finite difference methods; time stepping schemes; spectral methods. Computation methods for data analysis: Frequency analysis; singular value decomposition and principal component analysis; dynamic mode decomposition. Introduction to machine learning. Also known as: (formerly Civil Engineering 619.07)