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1PDEBENCH : an extensive benchmark for scientific machine learning
M. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, and M. Niepert. NIPS '22 : Proceedings of the 36th International Conference on Neural Information Processing Systems, page 1596-1611. Red Hook, Curran Associates Inc, (2022)
4 months ago by @unibiblio
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11OpenDiHu : an efficient and scalable framework for biophysical simulations of the neuromuscular system
B. Maier, D. Göddeke, F. Huber, T. Klotz, O. Röhrle, and M. Schulte. Journal of computational science, 79 (July): 102291 (2024)
6 months ago by @unibiblio
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11Research Data Management in Simulation Science : Infrastructure, Tools, and Applications
B. Flemisch, S. Hermann, M. Herschel, D. Pflüger, J. Pleiss, J. Range, S. Roy, M. Takamoto, and B. Uekermann. Datenbank-Spektrum, 24 (2): 97-105 (2024)
7 months ago by @unibiblio
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4Putting Co-Design-Supporting Data Lakes to the Test : An Evaluation on AEC Case Studies
M. Herschel, A. Gienger, A. Lauer, C. Stein, L. Skoury, N. Lässig, C. Ellwein, A. Verl, T. Wortmann, and C. Tarin Sauer. Big Data Analytics and Knowledge Discovery, 14912, Cham, Springer, (2024)
7 months ago by @unibiblio
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11OpenDiHu : An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System
B. Maier, D. Göddeke, F. Huber, T. Klotz, O. Röhrle, and M. Schulte. Journal of computational science, 79 (July): 102291 (2024)
7 months ago by @unibiblio
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2Coupled simulations and parameter inversion for neural system and electrophysiological muscle models
C. Homs Pons, R. Lautenschlager, L. Schmid, J. Ernst, D. Göddeke, O. Röhrle, and M. Schulte. GAMM-Mitteilungen, 47 (3): e202370009 (2024)
7 months ago by @unibiblio
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5Partitioned Coupling Schemes for Free-Flow and Porous-Media Applications with Sharp Interfaces
A. Jaust, K. Weishaupt, M. Mehl, and B. Flemisch. Finite Volumes for Complex Applications IX : Methods, Theoretical Aspects, Examples, 323, page 605-613. Cham, Springer, (2020)
a year ago by @unibiblio
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1POD-DEIM Model Order Reduction for the Monodomain Reaction-Diffusion Sub-Model of the Neuro-Muscular System
N. Emamy, P. Litty, T. Klotz, M. Mehl, and O. Röhrle. IUTAM Symposium on Model Order Reduction of Coupled Systems, volume 36 of IUTAM Bookseries, page 177-190. Springer Nature, (2019)
5 years ago by @unibiblio
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1Optimality principles in human point-to-manifold reaching accounting for muscle dynamics
I. Wochner, D. Drieß, H. Zimmermann, D. Haeufle, M. Toussaint, and S. Schmitt. Frontiers in Computational Neuroscience, (2020)
5 years ago by @unibiblio
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1Towards realistic HPC models of the neuromuscular system
C. Bradley, N. Emamy, T. Ertl, D. Göddeke, A. Hessenthaler, T. Klotz, A. Krämer, M. Krone, B. Maier, and M. Mehl. Preprint, (2018)
6 years ago by @unibiblio
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publications (hide)10
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1PDEBENCH : an extensive benchmark for scientific machine learning

11OpenDiHu : an efficient and scalable framework for biophysical simulations of the neuromuscular system

11Research Data Management in Simulation Science : Infrastructure, Tools, and Applications

4Putting Co-Design-Supporting Data Lakes to the Test : An Evaluation on AEC Case Studies

11OpenDiHu : An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System

2Coupled simulations and parameter inversion for neural system and electrophysiological muscle models

5Partitioned Coupling Schemes for Free-Flow and Porous-Media Applications with Sharp Interfaces

1POD-DEIM Model Order Reduction for the Monodomain Reaction-Diffusion Sub-Model of the Neuro-Muscular System

1Optimality principles in human point-to-manifold reaching accounting for muscle dynamics

1Towards realistic HPC models of the neuromuscular system

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