Dieses Bild zeigt Miriam Schulte

Miriam Schulte

Frau Prof. Dr. rer. nat. habil.

Institutsleiter
IPVS
Simulation großer Systeme

Kontakt

+49 711 685 88465
 +49 711 685 70413

Universitätsstraße 38
70569 Stuttgart
Deutschland
Raum: 2.125

Sprechstunde

Nach Vereinbarung

  1. Huber, F., Bürkner, P.-C., Göddeke, D., & Schulte, M. (2024). Knowledge-based modeling of simulation behavior for Bayesian optimization. Computational Mechanics, 74, Article 1. https://doi.org/10.1007/s00466-023-02427-3
  2. Homs‐Pons, C., Lautenschlager, R., Schmid, L., Ernst, J., Göddeke, D., Röhrle, O., & Schulte, M. (2024). Coupled simulations and parameter inversion for neural system and electrophysiological muscle models. GAMM-Mitteilungen. https://doi.org/10.1002/gamm.202370009
  3. Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., & Schulte, M. (2024). OpenDiHu: An efficient and scalable framework for biophysical simulations of the neuromuscular system. Journal of Computational Science, 79, 102291. https://doi.org/10.1016/j.jocs.2024.102291
  4. Davis, K., Leiteritz, R., Pflüger, D., & Schulte, M. (2023). Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps.
  5. Maier, B., Schneider, D., Schulte, M., & Uekermann, B. (2023). Bridging scales with volume coupling --- Scalable simulations of muscle contraction and electromyography. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Eds.), High Performance Computing in Science and Engineering ’21 (pp. 185–199). Springer International Publishing.
  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. (2022). preCICE v2: A sustainable and user-friendly coupling library. Open Research Europe, 2, 51. https://doi.org/10.12688/openreseurope.14445.2
  7. Schmidt, P., Jaust, A., Steeb, H., & Schulte, M. (2022). Simulation of flow in deformable fractures using a quasi-Newton based partitioned coupling approach. Computational Geosciences, 26, Article 2. https://doi.org/10.1007/s10596-021-10120-8
  8. Maier, B., & Schulte, M. (2022). Mesh generation and multi-scale simulation of a contracting muscle–tendon complex. Journal of Computational Science, 59, 101559. https://doi.org/10.1016/j.jocs.2022.101559
  9. Davis, K., Schulte, M., & Uekermann, B. (2022). Enhancing Quasi-Newton Acceleration for Fluid-Structure Interaction. Mathematical and Computational Applications, 27, Article 3. https://doi.org/10.3390/mca27030040
  10. Himthani, N., Brunn, M., Kim, J.-Y., Schulte, M., Mang, A., & Biros, G. (2022). CLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications. Journal of Imaging, 8, Article 9. https://doi.org/10.3390/jimaging8090251
  11. Leiteritz, R., Davis, K., Schulte, M., & Pflüger, D. (2022). A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps.
  12. Halilovic, S., Odersky, L., Böttcher, F., Davis, K., Schulte, M., Zosseder, K., & Hamacher, T. (2022). Optimization of an Energy System Model Coupled with a Numerical Hydrothermal Groundwater Simulation. 43rd IAEE International Conference.
  13. Totounferoush, A., Pour, N. E., Roller, S., & Mehl, M. (2021). Parallel Machine Learning of Partial Differential Equations. 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 698–703. https://doi.org/10.1109/IPDPSW52791.2021.00106
  14. Brunn, M., Himthani, N., Biros, G., Mehl, M., & Mang, A. (2021). Fast GPU 3D diffeomorphic image registration. Journal of Parallel and Distributed Computing, 149, 149–162. https://doi.org/10.1016/j.jpdc.2020.11.006
  15. Totounferoush, A., Simonis, F., Uekermann, B., & Schulte, M. (2021). Efficient and Scalable Initialization of Partitioned Coupled Simulations with preCICE. Algorithms, 14, Article 6. https://doi.org/10.3390/a14060166
  16. Brunn, M., Himthani, N., Biros, G., Mehl, M., & Mang, A. (2021). CLAIRE: Constrained Large Deformation Diffeomorphic Image Registration on Parallel Computing Architectures. Journal of Open Source Software, 6, Article 61. https://doi.org/10.21105/joss.03038
  17. Totounferoush, A., Ebrahimi Pour, N., Schröder, J., Roller, S., & Mehl, M. (2021). A data-based inter-code load balancing method for partitioned solvers. Journal of Computational Science, 51, 101329. https://doi.org/10.1016/j.jocs.2021.101329
  18. Totounferoush, A., Naseri, A., Chiva, J., Oliva, A., & Mehl, M. (2021). A GPU Accelerated Framework for Partitioned Solution of Fluid-Structure Interaction Problems. 14th WCCM-ECCOMAS Congress. https://doi.org/10.23967/wccm-eccomas.2020.021
  19. Totounferoush, A., Schumacher, A., & Schulte, M. (2021). Partitioned Deep Learning of Fluid-Structure Interaction.
  20. Maier, B., Stach, M., & Mehl, M. (2021). Real-Time, Dynamic Simulation of Deformable Linear Objects with Friction on a 2D Surface. In J. Billingsley & P. Brett (Eds.), Mechatronics and Machine Vision in Practice 4 (pp. 217–231). Springer International Publishing. 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. (2020). ExaFSA: Parallel Fluid-Structure-Acoustic Simulation. In H.-J. Bungartz, S. Reiz, B. Uekermann, P. Neumann, & W. E. Nagel (Eds.), Software for Exascale Computing - SPPEXA 2016-2019 (pp. 271–300). Springer International Publishing.
  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. (2020). Umgang mit Forschungssoftware an der Universität Stuttgart. Universität Stuttgart. https://doi.org/10.18419/OPUS-11178
  23. Emamy, N., Litty, P., Klotz, T., Mehl, M., & Röhrle, O. (2020). POD-DEIM Model Order Reduction for the Monodomain Reaction-Diffusion Sub-Model of the Neuro-Muscular System. In J. Fehr & B. Haasdonk (Eds.), IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22--25, 2018 (pp. 177–190). Springer International Publishing.
  24. Subramanian, S., Scheufele, K., Mehl, M., & Biros, G. (2020). Where did the tumor start? An inverse solver with sparse localization for tumor growth models. Inverse Problems, 36, Article 4. https://doi.org/10.1088/1361-6420/ab649c
  25. Rüth, B., Uekermann, B., Mehl, M., Birken, P., Monge, A., & Bungartz, H.-J. (2020). Quasi‐Newton waveform iteration for partitioned surface‐coupled multiphysics applications. International Journal for Numerical Methods in Engineering. https://doi.org/10.1002/nme.6443
  26. Brunn, M., Himthani, N., Biros, G., Mehl, M., & Mang, A. (2020). Multi-Node Multi-GPU Diffeomorphic Image Registration for Large-Scale Imaging Problems. SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, 1–17. https://doi.org/10.1109/SC41405.2020.00042
  27. Naseri, A., Totounferoush, A., González, I., Mehl, M., & Pérez-Segarra, C. D. (2020). A scalable framework for the partitioned solution of fluid--structure interaction problems. Computational Mechanics, 66, Article 2. https://doi.org/10.1007/s00466-020-01860-y
  28. Naseri, A., Totounferoush, A., González, I., Mehl, M., & Pérez-Segarra, C. D. (2020). A scalable framework for the partitioned solution of fluid--structure interaction problems. Computational Mechanics, 66, Article 2. https://doi.org/10.1007/s00466-020-01860-y
  29. Totounferoush, A., Ebrahimi Pour, N., Schröder, J., Roller, S., & Mehl, M. (2019). A New Load Balancing Approach for Coupled Multi-Physics Simulations. 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 676–682. https://doi.org/10.1109/IPDPSW.2019.00115
  30. Hirschmann, S., Brunn, M., Lahnert, M., Mehl, M., Glass, C. W., & Pflüger, D. (2017). Load balancing with p4est for Short-Range Molecular Dynamics with ESPResSo. Advances in Parallel Computing, 32, 455–464. 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. (2017). Towards realistic HPC models of the neuromuscular system. Frontiers in Physiology. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1772
  32. Scheufel, K., & Mehl, M. (2017). Robust multi-secant Quasi- Newton variants for parallel fluid-structure simulations -- and other multiphysics applications. SIAM Journal on Scientific Computing, 39, 404–433. https://doi.org/10.1137/16M1082020
  33. Gholami, A., Mang, A., Scheufele, K., Davatzikos, C., Mehl, M., & Biros, G. (2017). 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. https://doi.org/10.1145/3126908.3126930
  34. Bungartz, H.-J., Lindner, F., Gatzhammer, B., Mehl, M., Scheufele, K., Shukaev, A., & Uekermann, B. (2016). preCICE – A fully parallel library for multi-physics surface coupling. Computers & Fluids, 141, 250–258. https://doi.org/10.1016/j.compfluid.2016.04.003
  35. Bader, M., Bungartz, H.-J., & Mehl, M. (2011). Space-Filling Curves. Encyclopedia of Parallel Computing, 1862–1867. https://doi.org/10.1007/978-0-387-09766-4_145
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