Kategorie: ‘Student CVs’
Andrea Hanke, M.Sc.
Contact
Aachen Institute for Advanced Study
in Computational Engineering Science (AICES)
RWTH Aachen
Schinkelstr. 2
52062 Aachen
Office: Room 421b
Phone: +49 241 80 99140
Email: hanke@aices.rwth-aachen.de
Education
01/2019 – Current: Ph.D. Candidate, IRTG at RWTH Aachen University, Germany
10/2016 – 12/2018 Master of Science in Simulation Sciences, RWTH Aachen University, Germany
10/2013 – 09/2016 Bachelor of Science in Material Sciences, RWTH Aachen University, Germany
Theses
- Understanding the Properties in Resistive Switching Oxides. (Master’s Thesis at the PGI of the Forschungszentrum Jülich)
- A Computational Study of the Structural and Electronic Properties of Amorphous Antimony (Bachelor’s Thesis at RWTH Aachen University)
Ankit Chakraborty, M.Sc.
Contact
Aachen Institute for Advanced Study
in Computational Engineering Science (AICES)
RWTH Aachen
Schinkelstr. 2
52062 Aachen
Office: Room 424
Phone: +49 241 80 99136
Email: chakraborty@aices.rwth-aachen.de
Education
09/2018 – Current: Ph.D. Candidate, IRTG at RWTH Aachen University, Germany
10/2015 – 05/2018: Master of Science in Simulation Science, RWTH, Aachen, Germany
2011 – 2015: Bachelor of Technology in Mechanical Engineering at Indian Institute of Technology, Patna, India
Research Interests
Higher Order Galerkin Schemes and Metric based Anisotropic Mesh Optimization.
Theses
- Numerical Modelling of Bi-material Specimen using Extended Finite Element Discretization under Thermo-mechanical Loading. (Bachelor’s Thesis, Indian Institute of Technology, Patna, 2015).
- A Continuous-mesh Optimization Technique for Piece-wise Polynomial Approximation on Tetrahedral Grids (Master’s Thesis, AICES, RWTH Aachen, Germany, 2018).
Publications
- An Anisotropic H-Adaptive Strategy for Discontinuous Petrov-Galerkin Schemes using a Continuous Mesh Model
A. Chakraborty, A. Rangarajan and G. May
Computer & Mathematics with Applications, 2022, 106
doi: https://doi.org/10.1016/j.camwa.2021.12.001 - A Residual-Based HP-Mesh Optimization Technique for Petrov-Galerkin Schemes with Optimal Test Functions
A. Chakraborty, A. Rangarajan and G. May
doi: http://dx.doi.org/10.23967/wccm-eccomas.2020.014
- A Goal Oriented Optimization Technique for Tetrahedral Grids using a Continuous- Mesh Model
A. Rangarajan, A. Chakraborty and G. May
AIAA Scitech 2019 Forum
doi: https://doi.org/10.2514/6.2019-0349
Karsten Paul, M.Sc.
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 – 05/2022: Ph.D. Candidate, IRTG 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
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)
Publications
- An adaptive space-time phase field formulation for dynamic fracture of brittle shells
based on LR NURBS
K. Paul, C. Zimmermann, K.K. Mandadapu, T.J.R. Hughes, C.M. Landis and R.A. Sauer
Computational Mechanics, 2020, 65, pp. 1039-1062
doi: 10.1007/s00466-019-01807-y - Isogeometric continuity constraints for multi-patch shells governed by fourth-order
deformation and phase field models
K. Paul, C. Zimmermann, T.X. Duong and R.A. Sauer
Computer Methods in Applied Mechanics and Engineering, 2020, 370, pp. 113219
doi: 10.1016/j.cma.2020.113219 - Dynamic Fracture of Brittle Shells in a Space-Time Adaptive Isogeometric Phase Field
Framework
K. Paul, T.J.R. Hughes, C.M. Landis and R.A. Sauer
CurrentTrendsandOpenProblemsinComputationalMechanics, 2022, pp. 407-415, Springer
Nature. - An isogeometric finite element formulation for surface and shell viscoelasticity based
on a multiplicative surface deformation split
K. Paul and R.A. Sauer
Preprint, https://arxiv.org/abs/2202.13413
Tim Varelmann, M.Sc.
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 – 03/2022: Ph.D. Candidate, IRTG at RWTH Aachen University, Germany
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
Publication
- A decoupling strategy for protecting sensitive process information in cooperative optimization of power flow
T. Varelmann, J.I. Otashu, K. Seo, A .W. Lipow, A. Mitsos and M. Baldea
AIChE Journal, 2021; e17429.
doi: https://doi.org/10.1002/aic.17429 - Simultaneously optimizing bidding strategy in pay-as-bid-markets and production scheduling
T. Varelmann, N. Erwes, P. Schäfer and A. Mitsos
Computers and Chemical Engineering, 2022, (157), 107610
doi: 10.1016/j.compchemeng.2021.107610
- Advanced feasibility cuts in decoupled cooperative optimization of power flow
T. Varelmann, A .W. Lipow, M. Baldea and A. Mitsos
Computers and Chemical Engineering, 2021, 107635
doi: 10.1016/j.compchemeng.2021.107635
Cristos Psarras, M.Sc.
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: Ph.D. Candidate, IRTG at RWTH Aachen University, Germany
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
Publications
- Linnea: Automatic Generation of Efficient Linear Algebra
H. Barthels, C. Psarras and P. Bientinesi
Programs. ACM Trans. Math. Softw. 47, 3, Article 22 (June 2021), 26 pages.
doi: https://doi.org/10.1145/3446632 - Accelerating jackknife resampling for the Canonical Polyadic Decomposition
C. Psarras, L. Karlsson, R. Bro and P. Bientinesi
Frontiers in Applied Mathematics and Statistics, accepted (Feb 2022)
Preprint, arXiv:2112.03985 - Concurrent Alternating Least Squares for multiple simultaneous Canonical Polyadic
Decompositions
C. Psarras, L. Karlsson and P. Bientinesi
ACM Transactions on Mathematical Software, accepted (Feb 2022)
Preprint, arXiv:2010.04678 - The Linear Algebra Mapping Problem
C. Psarras, H. Barthels and P. Bientinesi
Preprint, arXiv:1911.09421
Submitted to ACM Transactions on Mathematical Software (Sep 2021) - The landscape of software for tensor computations
C. Psarras, L. Karlsson, J. Li and P. Bientinesi
Preprint, arXiv:2103.13756
Nicole Aretz, M.Sc.
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
- 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.