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Project Summary

Monte Carlo methods are the computational workhorse of statistical inference as applied to inverse problems throughout the physical sciences, but can become prohibitively costly when inferring high-dimensional or coupled parameters. This is exactly the setting of many state or parameter inference problems associated to multiscale systems of interest to SFB 1114. We propose to use a combination of strategies drawn from established traditions such as multilevel and adaptive Monte Carlo, and novel contributions such as likelihood-informed active subspace dimension reduction, to reduce the effective computational dimension, thereby accelerating convergence and reducing computational cost, while also studying and controlling the impact of the approximation errors incurred.

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Project Details

  • Status:  In Progress
  • Head(s):  Prof. Sullivan
  • Participating Institution(s):  FU Berlin, Zuse Institute Berlin (ZIB)
  • Area:  A: Efficient modelling of macro scales
  • Positions available?: 
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