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IRTG Modern Inverse Problems (MIP)

Kategorie: ‘Student CVs’

Karsten Paul

31. Oktober 2018 | von

Contact

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

Office: Room 431a
Phone: +49 241 80 99138
Email: paul@aices.rwth-aachen.de

LinkedIn: https://www.linkedin.com/in/karsten-paul-b7b08818b/

XING: https://www.xing.com/profile/Karsten_Paul7/cv

Education

12/2018 – Current: PhD Student at RWTH Aachen University, Germany

04/2017 – 11/2018: Master of Science in Computational Engineering Science, RWTH Aachen University, Germany

10/2013 – 03/2017: Bachelor of Science in Computational Engineering Science, RWTH Aachen University, Germany

Professional Career

10/2017 – 07/2018: Student Assistant at fka Aachen, Germany

09/2016 – 03/2017: Intern at Volkswagen AG Brunswick, Germany

Publications

  • Paul, K., Zimmermann, C., Duong, T.X., Sauer, R.A.: “Isogeometric continuity constraints for multi-patch shells governed by fourth-order deformation and phase field models”, arXiv:2001.05964, preprint, 2020
  • Paul, K., Zimmermann, C., Mandadapu, K.K., Hughes, T.J.R., Landis, C.M., and Sauer, R.A.: “An adaptive space-time phase field formulation for dynamic fracture of brittle shells based on LR NURBS”, Computational Mechanics, in press, 2020
  • Franke, T., Fiebig, S., Paul, K., Vietor, T., and Sellschopp, J.: “Topology Optimization with Integrated Casting Simulation and Parallel Manufacturing Process Improvement”, in A. Schumacher, T. Vietor, S. Fiebig, K.-U. Bletzinger, and K. Maute, editors, Advances in Structural and Multidisciplinary Optimization, pages 1815-1830, Springer International Publishing, 2018

Theses

  • Phase Field Modeling of Dynamic Brittle Fracture in Thin Shells (Master Thesis, AICES, RWTH Aachen University, Germany 2018)
  • Optimization of Casting Direction as well as Positioning and Dimensioning of Feeders using an Innovative Graph-Based Target Function for the use in a Topology Optimization suitable for Casting (Bachelor Thesis, Volkswagen AG Brunswick, Germany 2017)

Tim Varelmann

12. Oktober 2018 | von

Contact

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

Office: Room 424
Phone: +49 241 80 99137
Email: varelmann@aices.rwth-aachen.de

Education

11/2018 – Current:  PhD Student at RWTH Aachen University

04/2018-10/2018: Master Thesis at PSE Laboratory, Department of Chemical Engineering, MIT, USA

07/2017-04/2018: Master student at AICES Graduate School, RWTH Aachen University, Germany

Professional Career

03/2016 – 06/2016: Internship at LANXESS Deutschland GmbH, Process, Technology, Safety and Environment Department, Leverkusen, Germany

10/2014 – 03/2015: Teaching Assistant for „Material Properties“ with Prof. A. E. Ismail RWTH Aachen University, Germany

08/2012 – 09/2013: Dual Student of Business Engineering in cooperation with GE Wind Energy GmbH, Salzbergen, Germany

Cristos Psarras

12. Oktober 2018 | von

Contact

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

Office: Room 430
Phone: +49 241 80 99 142
Email: psarras@aices.rwth-aachen.de

 

Education

10/2018 – Current:  PhD Student at RWTH Aachen University

09/2011 – 07/2017: Diploma in Electrical & Computer Engineering at AUTH   (5 year academic program – Equivalent to M.Sc.)

 

Professional Career

10/2018 – Current  Researcher at RWTH Aachen University

03/2018 – 09/2018 Software Engineer at KENOTOM

06/2017 – 03/2018 Research Associate at Center for Research & Technology Hellas

03/2017 – 05/2017 Intern at Center for Research & Technology Hellas

 

Research Interests

  • High Performance Computing, Automation, Compilers, Machine Learning & Computer Vision

 

 

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: 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

  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. In Numerical Mathematics and Advanced Applications ENUMATH 2019 (pp. 489-497). Springer, Cham.
  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
  4. 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