Miriam Schulte
Miriam Schulte
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Zitiert von
Zitiert von
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ...
arXiv preprint arXiv:1811.02629, 2018
Multiphysics simulations: Challenges and opportunities
DE Keyes, LC McInnes, C Woodward, W Gropp, E Myra, M Pernice, J Bell, ...
The International Journal of High Performance Computing Applications 27 (1 …, 2013
Space-filling curves: an introduction with applications in scientific computing
M Bader
Springer Science & Business Media, 2012
preCICE–a fully parallel library for multi-physics surface coupling
HJ Bungartz, F Lindner, B Gatzhammer, M Mehl, K Scheufele, A Shukaev, ...
Computers & Fluids 141, 250-258, 2016
Peano—a traversal and storage scheme for octree-like adaptive Cartesian multiscale grids
T Weinzierl, M Mehl
SIAM Journal on Scientific Computing 33 (5), 2732-2760, 2011
Parallel coupling numerics for partitioned fluid–structure interaction simulations
M Mehl, B Uekermann, H Bijl, D Blom, B Gatzhammer, A Van Zuijlen
Computers & Mathematics with Applications 71 (4), 869-891, 2016
A cache-aware algorithm for PDEs on hierarchical data structures based on space-filling curves
F Günther, M Mehl, M Pögl, C Zenger
SIAM Journal on Scientific Computing 28 (5), 1634-1650, 2006
The PDE framework Peano applied to fluid dynamics: an efficient implementation of a parallel multiscale fluid dynamics solver on octree-like adaptive Cartesian grids
HJ Bungartz, M Mehl, T Neckel, T Weinzierl
Computational Mechanics 46, 103-114, 2010
preCICE v2: A sustainable and user-friendly coupling library
G Chourdakis, K Davis, B Rodenberg, M Schulte, F Simonis, ...
Open Research Europe 2, 2022
Improving the performance of the partitioned QN-ILS procedure for fluid–structure interaction problems: Filtering
R Haelterman, AEJ Bogaers, K Scheufele, B Uekermann, M Mehl
Computers & Structures 171, 9-17, 2016
A cache‐oblivious self‐adaptive full multigrid method
M Mehl, T Weinzierl, C Zenger
Numerical Linear Algebra with Applications 13 (2‐3), 275-291, 2006
A parallel adaptive Cartesian PDE solver using space–filling curves
HJ Bungartz, M Mehl, T Weinzierl
European Conference on Parallel Processing, 1064-1074, 2006
A comparison of various quasi-Newton schemes for partitioned fluid-structure interaction
F Lindner, M Mehl, K Scheufele, B Uekermann
Coupled VI: Proceedings of the VI International Conference on Computational …, 2015
Numerical simulation of particle transport in a drift ratchet
M Brenk, HJ Bungartz, M Mehl, IL Muntean, T Neckel, T Weinzierl
SIAM Journal on Scientific Computing 30 (6), 2777-2798, 2008
Reinforcement learning for call admission control and routing in integrated service networks
P Marbach, O Mihatsch, M Schulte, J Tsitsiklis
Advances in Neural Information Processing Systems 10, 1997
Fluid-structure interaction on cartesian grids: Flow simulation and coupling environment
M Brenk, HJ Bungartz, M Mehl, T Neckel
Fluid-Structure Interaction: Modelling, Simulation, Optimisation, 233-269, 2006
A plug-and-play coupling approach for parallel multi-field simulations
HJ Bungartz, F Lindner, M Mehl, B Uekermann
Computational Mechanics 55, 1119-1129, 2015
Navier–Stokes and Lattice–Boltzmann on octree‐like grids in the Peano framework
M Mehl, T Neckel, P Neumann
International Journal for Numerical Methods in Fluids 65 (1‐3), 67-86, 2011
Partitioned simulation of fluid-structure interaction on cartesian grids
HJ Bungartz, J Benk, B Gatzhammer, M Mehl, T Neckel
Fluid Structure Interaction II: Modelling, Simulation, Optimization, 255-284, 2010
Time-resolved study of biofilm architecture and transport processes using experimental and simulation techniques: the role of EPS
M Kuehn, M Mehl, M Hausner, HJ Bungartz, S Wuertz
Water Science and Technology 43 (6), 143-151, 2001
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