MA50250: Inverse problems, data assimilation and filtering
[Page last updated: 23 October 2023]
Academic Year:  2023/24 
Owning Department/School:  Department of Mathematical Sciences 
Credits:  6 [equivalent to 12 CATS credits] 
Notional Study Hours:  120 
Level:  Masters UG & PG (FHEQ level 7) 
Period: 

Assessment Summary:  CWRI 100% 
Assessment Detail: 

Supplementary Assessment: 

Requisites:  Before taking this module you must take MA50174 OR take MA50178 OR take MA50281 or equivalent. 
Learning Outcomes: 
By the end of this unit, you will be able to

Aims:  To understand inverse problems and their importance in many areas of science, and to develop theoretical and practical understanding of data assimilation, filtering, and regularisation methods for solving inverse problems. 
Skills:  Problem solving (T, F&A), computing (T, F&A), written and oral communication (F&A). 
Content:  Inverse problems; illposedness and regularisation methods, Tikhonov regularisation and truncated singular value decomposition. Variational regularisation.
Statistical data assimilation and filtering; variational methods (3DVar/4DVar); Kalman filters. Applications, for example, in medical imaging, meteorology and oceanography. 
Course availability: 
MA50250 is Optional on the following courses:Department of Mathematical Sciences

Notes:
