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SUMMARY:Computational Advancements in Edge-Preserving Methods for Dynamic 
 and Large-Scale Inverse Problems - Mirjeta Pasha (Tufts University)
DTSTART:20230329T104000Z
DTEND:20230329T113000Z
UID:TALK198241@talks.cam.ac.uk
DESCRIPTION:Fast-developing fields such as data science\, uncertainty quan
 tification\, and machine learning rely on fast and accurate methods for in
 verse problems. Three emerging challenges on obtaining meaningful solution
 s to large-scale and data-intensive inverse problems are ill-posedness of 
 the problem\, large dimensionality of the parameters\, and the complexity 
 of the model constraints.\n&nbsp\;\nTackling the immediate challenges that
  arise from growing model complexities\n(spatiotemporal measurements) and 
 data-intensive studies (large-scale and high-dimensional measurements)\, s
 tate-of-the-art methods can easily exceed their limits of applicability.\n
 &nbsp\;\nIn this talk we discuss recent advancements on edge-preserving an
 d computationally efficient methods for computing solutions to dynamic inv
 erse problems\, where both the quantities of interest and the forward oper
 ator change at different time instances.\n&nbsp\;\nIn the first part of th
 e talk\, to remedy these difficulties\, we apply efficient regularization 
 methods that enforce simultaneous regularization in space and time (such a
 s edge enhancement at each time instant and proximity at consecutive time 
 instants) and achieve this with low computational cost and enhanced accura
 cy.\nIn the remainder of the talk\, we focus on designing spatio-temporal 
 Bayesian Besov priors for computing the MAP estimate in large-scale and dy
 namic inverse problems.\n&nbsp\;\nNumerical examples from a wide range of 
 applications\, such as tomographic reconstruction\, image deblurring\, and
  multichannel dynamic tomography are used to illustrate the effectiveness 
 of the described methods.
LOCATION:Seminar Room 1\, Newton Institute
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