GSC 111: Aachen Institute for Advanced Study in Computational Engineering Science (AICES)
Mathematics
Systems Engineering
Final Report Abstract
The graduate school “Aachen Institute for Advanced Study in Computational Engineering Science” (AICES) addresses concerns regarding graduate education in Germany: long time to degree, relatively high isolation during dissertation work, and insufficient international exposure. In AICES, each doctoral candidate is advised by a junior advisor and a senior co-advisor. A competitive fund allocation process encourages the formation of interdisciplinary mentoring teams and careful project planning. By involving existing and hiring new young researchers, the student-to-faculty ratio is improved, allowing for closer supervision and advising. Moreover, senior faculty members provide guidance not only to doctoral candidates but also to junior faculty. In a further effort to reduce the time to degree, excellent bachelor’s applicants follow a streamlined path to a doctorate through coordinated master’s and doctoral programs. AICES therefore has an impact on several stages of education: master’s, doctoral, and postdoctoral AICES is supported by existing university programs in computational engineering and simulation sciences. These include consecutive bachelor’s and master’s programs as well as a new non-consecutive master’s program. Additional structures focusing on simulation and highperformance computing operate within the Jülich-Aachen Research Alliance. AICES gathers the expertise of a diverse group of institutes with a strong history of collaboration in research and teaching. In the second funding period, AICES extends its coverage to three additional faculties, as well as to a number of additional young researchers at the integrated non-university partners. The scientific focus is placed on challenging modeling and simulation topics in the application areas considered: materials science, mechanical, chemical and biomedical engineering, geoscience, and expanding to biomedicine, civil and electrical engineering. This broad spectrum provides fertile ground for targeted research on topics of synthesis, concentrating on broadlydefined inverse problems. This concept, coming to life only in the context of computational engineering science, involves parameter estimation, model development, model interaction on multiple scales, as well as optimal design, control and operations of complex engineered systems. Modeling is supported by a focus on model order reduction, and by unique dedicated seed funding for experimental projects to be performed with on-campus partners, providing a jump-start for collaboration with experimental labs. AICES contributes to and complements university-wide policy to support personnel at early career stages, and to ensure gender equality across all disciplines as well as internationality.
Publications
- Few inputs can reprogram biological networks. Nature. 2011;478(7369):E4
Müller FJ, Schuppert A
(See online at https://doi.org/10.1038/nature10543) - Heliostat field optimization: A new computationally efficient model and biomimetic layout. Solar Energy, 2012;86(2):792-803
Noone CJ, Torrilhon M, Mitsos A
(See online at https://doi.org/10.1016/j.solener.2011.12.007) - An accurate moving boundary formulation in cut-cell methods, Journal of Computational Physics, 2013;235:786-809
Schneiders L, Hartmann D, Meinke M, Schröder W
(See online at https://doi.org/10.1016/j.jcp.2012.09.038) - Shallow two-component gravity-driven flows with vertical variation. Journal of Fluid Mechanics, 2013;714:434-62
Kowalski J, McElwaine JN
(See online at https://doi.org/10.1017/jfm.2012.489) - A kinetic approach to the sequence–aggregation relationship in disease-related protein assembly, The Journal of Physical Chemistry B, 2014;118(4): 1003-11
Barz B, Wales DJ, Strodel B
(See online at https://doi.org/10.1021/jp412648u) - Level-of-detail quad meshing, ACM Transactions on Graphics (TOG), 2014;33(6):184
Ebke HC, Campen M, Bommes D, Kobbelt L
(See online at https://doi.org/10.1145/2661229.2661240) - Multivariate McCormick relaxations, Journal of Global Optimization, 2014;59(2-3):633-62
Tsoukalas A, Mitsos A
(See online at https://doi.org/10.1007/s10898-014-0176-0) - Numerical comparison of isotropic hypo-and hyperelas- ticbased plasticity models with application to industrial forming processes, International Journal of Plasticity, 2014;63:18-48
Brepols T, Vladimirov IN, Reese S
(See online at https://doi.org/10.1016/j.ijplas.2014.06.003) - Recent advances in computational methodology for simulation of mechanical circulatory assist devices, Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2014;6(2):169-88
Marsden AL, Bazilevs Y, Long CC, Behr M
(See online at https://doi.org/10.1002/wsbm.1260) - A scalable, linear-time dynamic cutoff algorithm for molecular dynamics, In International Conference on High Performance Computing 2015 (pp. 155-70), Springer, Cham
Springer P, Ismail AE, Bientinesi P
(See online at https://doi.org/10.1007/978-3-319-20119-1_12) - Algorithmic differentiation of numerical methods: Tangent and adjoint solvers for parameterized systems of nonlinear, ACM Transactions on Mathematical Software (TOMS), 2015;41(4):26
Naumann U, Lotz J, Leppkes K, Towara M
(See online at https://doi.org/10.1145/2700820) - Analysis of pressure perturbation sources on a generic space launcher after-body in supersonic flow using zonal turbulence modeling and dynamic mode decomposition,, Physics of Fluids, 2015;27(1):016103
Statnikov V, Sayadi T, Meinke M, Schmid P, Schröder W
(See online at https://doi.org/10.1063/1.4906219) - Higher-order discrete adjoint ODE solver in C++ for dynamic optimization, Procedia Computer Science, 2015;51:256-65
Lotz J, Naumann U, Hannemann-Taḿas R, Ploch T, Mitsos A
(See online at https://doi.org/10.1016/j.procs.2015.05.237) - Time discrete geodesic paths in the space of images, SIAM Journal on Imaging Sciences, 2015;8(3):1457-88
Berkels B, Effland A, Rumpf M
(See online at https://doi.org/10.1137/140970719) - A numerical method for the computation of tangent vectors to 2×2 hyperbolic systems of conservation laws, Communications in Mathematical Sciences, 2016;14(3):683–704
Herty M, Piccoli B
(See online at https://doi.org/10.4310/CMS.2016.v14.n3.a5) - Advances in the simulation of protein aggregation at the atomistic scale, Journal of Physical Chemistry B, 2016;120:2991-9
Carballo-Pacheco M, Strodel B
(See online at https://doi.org/10.1021/acs.jpcb.6b00059) - Automatic implementation of material laws: Jacobian calculation in a finite element code with TAPENADE, Computers & Mathematics with Applications, 2016;72(11):2808-22
Zwicke F, Knechtges P, Behr M, Elgeti S
(See online at https://doi.org/10.1016/j.camwa.2016.10.010) - Certified reduced basis methods for parametrised distributed elliptic optimal control problems with control constraints. SIAM Journal on Scientific Computing. 2016;38(6):A3921-46
Bader E, Kärcher M, Grepl MA, Veroy K
(See online at https://doi.org/10.1137/16M1059898) - Deforming fluid domains within the finite element method: five meshbased tracking methods in comparison, Archives of Computational Methods in Engineering, 2016;23(2):323-61
Elgeti S, Sauerland H
(See online at https://doi.org/10.1007/s11831-015-9143-2) - Modeling nonequilibrium gas flow based on moment equations, Annual Review of Fluid Mechanics, 2016;48:429-58
Torrilhon M
(See online at https://doi.org/10.1146/annurev-fluid-122414-034259) - Non-linear shape optimiza- tion using local subspace projections, ACM Transactions on Graphics (TOG), 2016;35(4):87
Musialski P, Hafner C, Rist F, Birsak M, Wimmer M, Kobbelt L
(See online at https://doi.org/10.1145/2897824.2925886) - Two-scale FE–FFT-and phase-field-based computational modeling of bulk microstructural evolution and macroscopic material behavior, Computer Methods in Applied Mechanics and Engineering, 2016;305:89-110
Kochmann J, Wulfinghoff S, Reese S, Mianroodi JR, Svendsen B
(See online at https://doi.org/10.1016/j.cma.2016.03.001) - A certified trust region reduced basis approach to PDE- constrained optimization, SIAM Journal on Scientific Computing, 2017;39(5):S434-60
Qian E, Grepl M, Veroy K, Willcox K
(See online at https://doi.org/10.1137/16M1081981) - A posteriori error control for the binary Mumford-Shah model, Mathematics of Computation, 2017;86(306):1769-91
Berkels B, Effland A, Rumpf M
(See online at https://doi.org/10.1090/mcom/3138) - An ultraweak DPG method for viscoelastic fluids. Journal of Non-Newtonian Fluid Mechanics. 2017;247:107-22
Keith B, Knechtges P, Roberts N V, Elgeti S, Behr M, Demkowicz L
(See online at https://doi.org/10.1016/j.jnnfm.2017.06.006) - Direct particle–fluid simulation of Kolmogorov-lengthscale size particles in decaying isotropic turbulence, Journal of Fluid Mechanics, 2017;819:188-227
Schneiders L, Meinke M, Schröder W
(See online at https://doi.org/10.1017/jfm.2017.171) - Spatially varying heat flux driven close-contact melting – A Lagrangian approach, International Journal of Heat and Mass Transfer. 2017;115:1276-87
Schüller K, Kowalski J
(See online at https://doi.org/10.1016/j.ijheatmasstransfer.2017.08.092) - Streamline segment scaling behavior in a turbulent wavy channel flow. Experiments in Fluids, 2017;58(2):10
Rubbert A, Hennig F, Klaas M, Pitsch H, Schröder W, Peters N
(See online at https://doi.org/10.1007/s00348-016-2291-9) - Transient multiple particle simulations of char particle combustion, Fuel, 2017;199:289-98
Sayadi T, Farazi S, Kang S, Pitsch H
(See online at https://doi.org/10.1016/j.fuel.2017.02.096) - A systematic at- las of chaperome deregulation topologies across the human cancer landscape, PLoS Computational Biology, 2018;14(1):e1005890
Esfahani AH, Sverchkova A, Saez-Rodriguez J, Schuppert AA, Brehme M
(See online at https://doi.org/10.1371/journal.pcbi.1005890) - Optimal Deterministic Algorithm Generation, Journal of Global Optimization. 2018
Mitsos A, Najman J, Kevrekidis IG
(See online at https://doi.org/10.1007/s10898-018-0611-8) - Simplex space-time meshes in two-phase flow simulations, International Journal for Numerical Methods in Fluids, 2018;86:218-30
Karyofylli V, Frings M, Elgeti S, Behr M
(See online at https://doi.org/10.1002/fld.4414)
