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: nellesen@aices.rwth-aachen.de
Education
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 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
- 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
- Nicole Aretz-Nellesen, Peng Chen, Martin A. Grepl and Karen Veroy: A sequential sensor selection strategy for hyper-parameterized linear Bayesian inverse problems. In Numerical Mathematics and Advanced Applications ENUMATH 2019 (pp. 489-497). Springer, Cham.
- Alex Viguerie, Alessandro Veneziani, Guillermo Lorenzo, Davide Baroli, Nicole Aretz-Nellesen, Alessia Patton, Thomas E. Yankeelov, Alessandro Reali, Thomas J. R. Hughes & Ferdinando Auricchio: Diffusion–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
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Nicole Aretz, Peng Chen, Karen Veroy: Sensor selection for hyper-parameterized linear Bayesian inverse problems 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.