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IRTG Modern Inverse Problems (MIP)

Dr. Nicole Aretz

10. Oktober 2018 | von

Contact

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

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

Education

10/2018 – 06/2022: Ph.D. Candidate, IRTG at 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 basic methods
  • multilevel methods
  • parameter estimation

 

Theses

Data Assimilation and Sensor Selection for Configurable Forward Models: Challenges and Opportunities for Model Order Reduction Methods
Doctoral Thesis, IRTG-2379, RWTH Aachen, Germany, 2021

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

Puplications

  1. 3D-VAR for parameterized partial differential equations: a certified reduced basis approach
    Nicole Aretz-Nellesen, Martin A. Grepl, and Karen Veroy
    Advances in Computational Mathematics
    45, 2369-2400 (2019)
    doi: 10.1007/s10444-019-09713-w
  2. A sequential sensor selection strategy for hyper-parameterized linear Bayesian inverse problems
    Nicole Aretz-Nellesen, Peng Chen, Martin A. Grepl and Karen Veroy
    In Numerical Mathematics and Advanced Applications ENUMATH 2019 (pp. 489-497). Springer, Cham.
  3. Diffusion–reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study
    Alex ViguerieAlessandro VenezianiGuillermo LorenzoDavide BaroliNicole Aretz-NellesenAlessia PattonThomas E. YankeelovAlessandro RealiThomas J. R. Hughes & Ferdinando Auricchio
    Comput Mech (2020), 66 (5), pp. 1131–1152
    doi: 10.1007/s00466-020-01888-0
  4. Sensor selection for hyper-parameterized linear Bayesian inverse problems
    Nicole Aretz, Peng Chen, Karen Veroy
    PAMM 20.S1 (2021)
    doi: 10.1002/pamm.202000357

     

Recorded Talks

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