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+49 711 685 88465
+49 711 685 70413
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Universitätsstraße 38
70569 Stuttgart
Deutschland
Room: 2.125
Office Hours
on appointment
- Homs‐Pons, C., Lautenschlager, R., Schmid, L., Ernst, J., Göddeke, D., Röhrle, O., Schulte, M.: Coupled simulations and parameter inversion for neural system and electrophysiological muscle models. GAMM-Mitteilungen. (2024). https://doi.org/10.1002/gamm.202370009.
- Huber, F., Bürkner, P.-C., Göddeke, D., Schulte, M.: Knowledge-based modeling of simulation behavior for Bayesian optimization. Computational Mechanics. 74, 151–168 (2024). https://doi.org/10.1007/s00466-023-02427-3.
- Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., Schulte, M.: OpenDiHu: An efficient and scalable framework for biophysical simulations of the neuromuscular system. Journal of Computational Science. 79, 102291 (2024). https://doi.org/10.1016/j.jocs.2024.102291.
- Davis, K., Leiteritz, R., Pflüger, D., Schulte, M.: Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps, (2023).
- Maier, B., Schneider, D., Schulte, M., Uekermann, B.: Bridging scales with volume coupling --- Scalable simulations of muscle contraction and electromyography. In: Nagel, W.E., Kröner, D.H., and Resch, M.M. (eds.) High Performance Computing in Science and Engineering ’21. pp. 185–199. Springer International Publishing, Cham (2023).
- Chourdakis, G., Davis, K., Rodenberg, B., Schulte, M., Simonis, F., Uekermann, B., Abrams, G., Bungartz, H.-J., Cheung Yau, L., Desai, I., Eder, K., Hertrich, R., Lindner, F., Rusch, A., Sashko, D., Schneider, D., Totounferoush, A., Volland, D., Vollmer, P., Koseomur, O.Z.: preCICE v2: A sustainable and user-friendly coupling library. Open Research Europe. 2, 51 (2022). https://doi.org/10.12688/openreseurope.14445.2.
- Schmidt, P., Jaust, A., Steeb, H., Schulte, M.: Simulation of flow in deformable fractures using a quasi-Newton based partitioned coupling approach. Computational Geosciences. 26, 381–400 (2022). https://doi.org/10.1007/s10596-021-10120-8.
- Maier, B., Schulte, M.: Mesh generation and multi-scale simulation of a contracting muscle–tendon complex. Journal of Computational Science. 59, 101559 (2022). https://doi.org/https://doi.org/10.1016/j.jocs.2022.101559.
- Davis, K., Schulte, M., Uekermann, B.: Enhancing Quasi-Newton Acceleration for Fluid-Structure Interaction. Mathematical and Computational Applications. 27, (2022). https://doi.org/10.3390/mca27030040.
- Himthani, N., Brunn, M., Kim, J.-Y., Schulte, M., Mang, A., Biros, G.: CLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications. Journal of Imaging. 8, (2022). https://doi.org/10.3390/jimaging8090251.
- Leiteritz, R., Davis, K., Schulte, M., Pflüger, D.: A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps, (2022).
- Halilovic, S., Odersky, L., Böttcher, F., Davis, K., Schulte, M., Zosseder, K., Hamacher, T.: Optimization of an Energy System Model Coupled with a Numerical Hydrothermal Groundwater Simulation. In: 43rd IAEE International Conference (2022).
- Totounferoush, A., Pour, N.E., Roller, S., Mehl, M.: Parallel Machine Learning of Partial Differential Equations. In: 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 698–703 (2021). https://doi.org/10.1109/IPDPSW52791.2021.00106.
- Brunn, M., Himthani, N., Biros, G., Mehl, M., Mang, A.: Fast GPU 3D diffeomorphic image registration. Journal of Parallel and Distributed Computing. 149, 149–162 (2021). https://doi.org/10.1016/j.jpdc.2020.11.006.
- Totounferoush, A., Simonis, F., Uekermann, B., Schulte, M.: Efficient and Scalable Initialization of Partitioned Coupled Simulations with preCICE. Algorithms. 14, (2021). https://doi.org/10.3390/a14060166.
- Brunn, M., Himthani, N., Biros, G., Mehl, M., Mang, A.: CLAIRE: Constrained Large Deformation Diffeomorphic Image Registration on Parallel Computing Architectures. Journal of Open Source Software. 6, 3038 (2021). https://doi.org/10.21105/joss.03038.
- Totounferoush, A., Ebrahimi Pour, N., Schröder, J., Roller, S., Mehl, M.: A data-based inter-code load balancing method for partitioned solvers. Journal of Computational Science. 51, 101329 (2021). https://doi.org/https://doi.org/10.1016/j.jocs.2021.101329.
- Totounferoush, A., Naseri, A., Chiva, J., Oliva, A., Mehl, M.: A GPU Accelerated Framework for Partitioned Solution of Fluid-Structure Interaction Problems. In: 14th WCCM-ECCOMAS Congress. CIMNE (2021). https://doi.org/10.23967/wccm-eccomas.2020.021.
- Totounferoush, A., Schumacher, A., Schulte, M.: Partitioned Deep Learning of Fluid-Structure Interaction, (2021).
- Maier, B., Stach, M., Mehl, M.: Real-Time, Dynamic Simulation of Deformable Linear Objects with Friction on a 2D Surface. In: Billingsley, J. and Brett, P. (eds.) Mechatronics and Machine Vision in Practice 4. pp. 217–231. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-43703-9_18.
- Lindner, F., Totounferoush, A., Mehl, M., Uekermann, B., Pour, N.E., Krupp, V., Roller, S., Reimann, T., C. Sternel, D., Egawa, R., Takizawa, H., Simonis, F.: ExaFSA: Parallel Fluid-Structure-Acoustic Simulation. In: Bungartz, H.-J., Reiz, S., Uekermann, B., Neumann, P., and Nagel, W.E. (eds.) Software for Exascale Computing - SPPEXA 2016-2019. pp. 271–300. Springer International Publishing, Cham (2020).
- Flemisch, B., Hermann, S., Holm, C., Mehl, M., Reina, G., Uekermann, B., Boehringer, D., Ertl, T., Grad, J.-N., Iglezakis, D., Jaust, A., Koch, T., Seeland, A., Weeber, R., Weik, F., Weishaupt, K.: Umgang mit Forschungssoftware an der Universität Stuttgart. Universität Stuttgart (2020). https://doi.org/10.18419/OPUS-11178.
- Emamy, N., Litty, P., Klotz, T., Mehl, M., Röhrle, O.: POD-DEIM Model Order Reduction for the Monodomain Reaction-Diffusion Sub-Model of the Neuro-Muscular System. In: Fehr, J. and Haasdonk, B. (eds.) IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22--25, 2018. pp. 177–190. Springer International Publishing, Cham (2020).
- Subramanian, S., Scheufele, K., Mehl, M., Biros, G.: Where did the tumor start? An inverse solver with sparse localization for tumor growth models. Inverse Problems. 36, 45006 (2020). https://doi.org/10.1088/1361-6420/ab649c.
- Rüth, B., Uekermann, B., Mehl, M., Birken, P., Monge, A., Bungartz, H.-J.: Quasi‐Newton waveform iteration for partitioned surface‐coupled multiphysics applications. International Journal for Numerical Methods in Engineering. (2020). https://doi.org/https://doi.org/10.1002/nme.6443.
- Brunn, M., Himthani, N., Biros, G., Mehl, M., Mang, A.: Multi-Node Multi-GPU Diffeomorphic Image Registration for Large-Scale Imaging Problems. In: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 1–17 (2020). https://doi.org/10.1109/SC41405.2020.00042.
- Naseri, A., Totounferoush, A., González, I., Mehl, M., Pérez-Segarra, C.D.: A scalable framework for the partitioned solution of fluid--structure interaction problems. Computational Mechanics. 66, 471–489 (2020). https://doi.org/10.1007/s00466-020-01860-y.
- Naseri, A., Totounferoush, A., González, I., Mehl, M., Pérez-Segarra, C.D.: A scalable framework for the partitioned solution of fluid--structure interaction problems. Computational Mechanics. 66, 471–489 (2020). https://doi.org/10.1007/s00466-020-01860-y.
- Totounferoush, A., Ebrahimi Pour, N., Schröder, J., Roller, S., Mehl, M.: A New Load Balancing Approach for Coupled Multi-Physics Simulations. In: 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 676–682 (2019). https://doi.org/10.1109/IPDPSW.2019.00115.
- Hirschmann, S., Brunn, M., Lahnert, M., Mehl, M., Glass, C.W., Pflüger, D.: Load balancing with p4est for Short-Range Molecular Dynamics with ESPResSo. Advances in Parallel Computing. 32, 455–464 (2017). https://doi.org/10.3233/978-1-61499-843-3-455.
- Bradley, C., Emamy, N., Göddeke, D., Klotz, T., Krämer, A., Krone, M., Maier, B., Mehl, M., Rau, T., Röhrle, O.: Towards realistic HPC models of the neuromuscular system. Frontiers in Physiology. (2017).
- Scheufel, K., Mehl, M.: Robust multi-secant Quasi- Newton variants for parallel fluid-structure simulations -- and other multiphysics applications. SIAM Journal on Scientific Computing. 39, 404–433 (2017). https://doi.org/10.1137/16M1082020.
- Gholami, A., Mang, A., Scheufele, K., Davatzikos, C., Mehl, M., Biros, G.: A Framework for Scalable Biophysics-based Image Analysis. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis SC17. 19:1––19:13 (2017). https://doi.org/10.1145/3126908.3126930.
- Bungartz, H.-J., Lindner, F., Gatzhammer, B., Mehl, M., Scheufele, K., Shukaev, A., Uekermann, B.: preCICE – A fully parallel library for multi-physics surface coupling. Computers & Fluids. 141, 250–258 (2016). https://doi.org/10.1016/j.compfluid.2016.04.003.
- Bader, M., Bungartz, H.-J., Mehl, M.: Space-Filling Curves. Encyclopedia of Parallel Computing. 1862–1867 (2011). https://doi.org/10.1007/978-0-387-09766-4_145.