Graduate and Doctoral Courses

Last Offered Spring 2024
Schedule & Location: T R 9:45-11:00, 422 DSL

Data assimilation methods combine numerical models and observations to arrive at the best possible representation of a physical system. This course aims to build a robust theoretical foundation in the subject and explore some of the computational challenges in large scientific and engineering applications. Students will gain hands-on experience by implementing their own algorithms and will complete a final project on a preferred research topic. Prerequisites: Applied Statistics for Engineers and Scientists (STA 3032), Applied Linear Algebra I/II (MAS 3105/MAS 4106) and Programming for Scientific Applications (ISC 4304) or Instructor Permission Required.