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 – present: 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
- 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 - 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. - Diffusion–reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study
Alex Viguerie, Alessandro Veneziani, Guillermo Lorenzo, Davide Baroli, Nicole Aretz-Nellesen, Alessia Patton, Thomas E. Yankeelov, Alessandro Reali, Thomas J. R. Hughes & Ferdinando Auricchio
Comput Mech (2020), 66 (5), pp. 1131–1152
doi: 10.1007/s00466-020-01888-0 -
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
- Nicole Aretz-Nellesen, Peng Chen, Martin A. Grepl, Karen Veroy: „Sensor Selection for Bayesian Inverse Problems and Data Assimilation”. Virtual Talk at ICERM workshop “Algorithms for Dimension and Complexity Reduction, March 26, 2020
- Nicole Aretz-Nellesen, Peng Chen, Denise Degen, Martin A. Grepl, Karen Veroy: „A sensor selection algorithm for hyper parameterized linear bayesian inverse problems“ virtual talk at WCCM-ECOOMAS Congress 2020.