IRTG Modern Inverse Problems (MIP)

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 basis methods
• multilevel methods
• parameter estimation

Puplications

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 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