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Huber , Felix

Felix Huber

M.Sc.

Research assistant
Institute of Applied Analysis and Numerical Simulation
Chair Computational Mathematics for Complex Simulation in Science and Engineering

Contact

+49 711 685 60125
+49 711 685 52022
Email

Allmandring 5b
70569 Stuttgart
Deutschland
Room: 1.43

Publications

B. Maier, D. Göddeke, F. Huber, T. Klotz, O. Röhrle, and M. Schulte, “OpenDiHu: An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System,” Journal of Computational Science, vol. 79, 2024, doi: https://doi.org/10.1016/j.jocs.2024.102291.
- BibTeX
BibTeX
@article{Maier2024OpenDiHu, author = {Maier, Benjamin and Göddeke, Dominik and Huber, Felix and Klotz, Thomas and Röhrle, Oliver and Schulte, Miriam}, doi = {https://doi.org/10.1016/j.jocs.2024.102291}, journal = {Journal of Computational Science}, title = {OpenDiHu: An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System}, volume = 79, year = 2024 }
F. Huber, P.-C. Bürkner, D. Göddeke, and M. Schulte, “Knowledge-based modeling of simulation behavior for Bayesian optimization,” Computational Mechanics, vol. 74, no. 1, Art. no. 1, 2024, doi: 10.1007/s00466-023-02427-3.
- BibTeX
- Link
BibTeX
@article{Huber2024, abstract = {Numerical simulations consist of many components that affect the simulation accuracy and the required computational resources. However, finding an optimal combination of components and their parameters under constraints can be a difficult, time-consuming and often manual process. Classical adaptivity does not fully solve the problem, as it comes with significant implementation cost and is difficult to expand to multi-dimensional parameter spaces. Also, many existing data-based optimization approaches treat the optimization problem as a black-box, thus requiring a large amount of data. We present a constrained, model-based Bayesian optimization approach that avoids black-box models by leveraging existing knowledge about the simulation components and properties of the simulation behavior. The main focus of this paper is on the stochastic modeling ansatz for simulation error and run time as optimization objective and constraint, respectively. To account for data covering multiple orders of magnitude, our approach operates on a logarithmic scale. The models use a priori knowledge of the simulation components such as convergence orders and run time estimates. Together with suitable priors for the model parameters, the model is able to make accurate predictions of the simulation behavior. Reliably modeling the simulation behavior yields a fast optimization procedure because it enables the optimizer to quickly indicate promising parameter values. We test our approach experimentally using the multi-scale muscle simulation framework OpenDiHu and show that we successfully optimize the time step widths in a time splitting approach in terms of minimizing the overall error under run time constraints.}, author = {Huber, Felix and Bürkner, Paul-Christian and Göddeke, Dominik and Schulte, Miriam}, doi = {10.1007/s00466-023-02427-3}, issn = {1432-0924}, journal = {Computational Mechanics}, number = 1, pages = {151--168}, title = {Knowledge-based modeling of simulation behavior for Bayesian optimization}, url = {https://doi.org/10.1007/s00466-023-02427-3}, volume = 74, year = 2024 }
Link
https://doi.org/10.1007/s00466-023-02427-3
A. Krämer et al., “Multi-physics multi-scale HPC simulations of skeletal muscles,” High Performance Computing in Science and Engineering ’20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020, pp. 185–203, 2021, doi: 10.1007/978-3-030-80602-6_13.
- BibTeX
BibTeX
@article{Kraemer:2021:MPM, author = {Krämer, Aaron and Maier, Benjamin and Rau, Tobias and Huber, Felix and Klotz, Thomas and Ertl, Thomas and Göddeke, Dominik and Mehl, Miriam and Reina, Guido and Röhrle, Oliver}, booktitle = {High Performance Computing in Science and Engineering '20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020}, chapter = 13, doi = {10.1007/978-3-030-80602-6_13}, editor = {Nagel, Wolfgang E. and Kröner, Dietmar H. and Resch, Michael M.}, pages = {185-203}, title = {Multi-physics multi-scale HPC simulations of skeletal muscles}, year = 2021 }
A. Krämer et al., “Multi-physics multi-scale HPC simulations of skeletal muscles,” High Performance Computing in Science and Engineering ’20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020, 2021, doi: 10.1007/978-3-030-80602-6_13.
- BibTeX
BibTeX
@article{Kraemer:2021:MPM, author = {Kr{\"a}mer, Aaron and Maier, Benjamin and Rau, Tobias and Huber, Felix and Klotz, Thomas and Ertl, Thomas and G{\"o}ddeke, Dominik and Mehl, Miriam and Reina, Guido and R{\"o}hrle, Oliver}, booktitle = {High Performance Computing in Science and Engineering '20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020}, chapter = 13, doi = {10.1007/978-3-030-80602-6_13}, editor = {Nagel, Wolfgang E. and Kröner, Dietmar H. and Resch, Michael M.}, title = {Multi-physics multi-scale HPC simulations of skeletal muscles}, year = 2021 }
F. Huber, “Efficient Algorithms for Geodesic Shooting in Diffeomorphic Image Registration,” master thesis, 2019. doi: https://dx.doi.org/10.18419/opus-10479.
- BibTeX
BibTeX
@mastersthesis{Huber2019Efficient, author = {Huber, Felix}, doi = {https://dx.doi.org/10.18419/opus-10479}, school = {Universität Stuttgart}, title = {Efficient Algorithms for Geodesic Shooting in Diffeomorphic Image Registration}, type = {master thesis}, year = 2019 }

Felix Huber

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Felix Huber

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