MA50250: Inverse problems, data assimilation and filtering
[Page last updated: 15 October 2020]
Academic Year:  2020/1 
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:  CW 70%, OR 30% 
Assessment Detail: 

Supplementary Assessment: 

Requisites:  Before taking this module you must take MA40198 AND ( take MA50174 OR take MA50178 ) 
Description:  Aims: Students should know why inverse problems are important in many areas of mathematics and its applications. They should be able to demonstrate theoretical and practical understanding of data assimilation, filtering and regularisation methods for solving inverse problems. Learning Outcomes: Students should be able to: Formulate inverse problems, regularise illposed problems, and analyse their structure; Construct, analyse, and interpret solutions to inverse problems using regularisation and statistical data assimilation techniques; Apply statistical filtering methods and interpret their solutions; Communicate problem descriptions, model formulations, and problem solutions. 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. Statistical data assimilation and filtering; variational methods (3DVar/4DVar); Kalman filters and extensions, particle filters. Applications, for example, in medical imaging, meteorology and oceanography. 
Programme availability: 
MA50250 is Optional on the following programmes:Department of Mathematical Sciences

Notes:
