This image shows Miriam Schulte

Miriam Schulte

Prof. Dr. rer. nat. habil.

Head of Institute
IPVS
Simulation of Large Systems

Contact

+49 711 685 88465
 +49 711 685 70413

Universitätsstraße 38
70569 Stuttgart
Deutschland
Room: 2.125

Office Hours

on appointment

  1. 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.
  2. 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.
  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.
  4. Davis, K., Leiteritz, R., Pflüger, D., Schulte, M.: Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps, (2023).
  5. 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).
  6. 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.
  7. 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.
  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.
  9. 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.
  10. 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.
  11. Leiteritz, R., Davis, K., Schulte, M., Pflüger, D.: A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps, (2022).
  12. 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).
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Totounferoush, A., Schumacher, A., Schulte, M.: Partitioned Deep Learning of Fluid-Structure Interaction, (2021).
  20. 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.
  21. 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).
  22. 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.
  23. 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).
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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).
  32. 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.
  33. 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.
  34. 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.
  35. 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.
To the top of the page