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.

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File	Description	File size
ISC5395 Spring 2014.pdf	Syllabus	53 kB