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SUMMARY:CUQIpy - Computational Uncertainty Quantification for Inverse prob
 lems in Python - Jakob Jørgensen (Technical University of Denmark)
DTSTART:20230329T085000Z
DTEND:20230329T094000Z
UID:TALK198235@talks.cam.ac.uk
DESCRIPTION:In this talk we present CUQIpy (pronounced &rdquo\;cookie pie&
 rdquo\;) - a new computational modelling environment in Python that uses u
 ncertainty quantification (UQ) to access and quantify the uncertainties in
  solutions to inverse problems. The overall goal of the software package i
 s to allow both expert and non-expert (without deep knowledge of statistic
 s and UQ) users to perform UQ related analysis of their inverse problem wh
 ile focusing on the modelling aspects. To achieve this goal the package ut
 ilizes state-of-the-art tools and methods in statistics and scientific com
 puting specifically tuned to the ill-posed and often large-scale nature of
  inverse problems to make UQ feasible. We showcase the software on problem
 s relevant to imaging science such as computed tomography and partial diff
 erential equation-based inverse problems. CUQIpy is developed as part of t
 he CUQI project at the Technical University of Denmark and is available at
  https://cuqi-dtu.github.io/CUQIpy/ .
LOCATION:Seminar Room 1\, Newton Institute
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