IRTG Modern Inverse Problems (MIP)

Nicole Nellesen

10. Oktober 2018 | von


Aachen Institute for Advanced Study
in Computational Engineering Science (AICES)
RWTH Aachen
Schinkelstr. 2
52062 Aachen

Office: Room 421b
Tel. (0241) 80 99145
Email: nellesen@aices.rwth-aachen.de


since 10/2018 Doctoral Student at IRTG-2379,
RWTH Aachen University, Germany

04/2018 – 09/2018 Doctoral Student at AICES Graduate School,
RWTH Aachen University, Germany

04/2016 – 03/2018 Master of Science in Mathematics,
RWTH Aachen University, Germany

10/2012 – 03/2016 Bachelor of Science in Mathematics,
RWTH Aachen University, Germany

Research Interests

  •  bayesian inversion
  • optimal experimental design
  • variational data assimilation and optimal control
  • uncertainty quantification
  • model order reduction, in particular reduced basis methods
  • multilevel methods
  • parameter estimation


A Certified Reduced Basis Method for Parametrized 3D-VAR Data Assimilation, 
Master’s Thesis, Institut für Geometrie und Praktische Mathematik, RWTH Aachen, Germany 2018

A Space-Time Finite Element Method for Discretization of the Heat Equation,
Bachelor’s Thesis, Institut für Geometrie und Praktische Mathematik, RWTH Aachen, Germany, 2016


  1. Nicole Aretz-Nellesen, Martin A. Grepl, and Karen Veroy. 3D-VAR for parameterized partial differential equations: a certified reduced basis approach. Advances in Computational Mathematics 45, 2369-2400 (2019) doi: 10.1007/s10444-019-09713-w
  2. Nicole Aretz-Nellesen, Peng Chen, Martin A. Grepl and Karen Veroy: A sequential sensor selection strategy for hyper-parameterized linear Bayesian inverse problems. Accepted by ENUMATH conference proceedings (2020)
  3. Alex ViguerieAlessandro VenezianiGuillermo LorenzoDavide BaroliNicole Aretz-NellesenAlessia PattonThomas E. YankeelovAlessandro RealiThomas J. R. Hughes & Ferdinando AuricchioDiffusion–reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study. Comput Mech (2020). doi: 10.1007/s00466-020-01888-0

Online Material

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