M. Sc. David Meier

AI for Computationally Intelligent Systems (AI4CIS)

Telephone: +49 561 804-6048
Fax: +49 561 804-6022
Email: meier.david[at]uni-kassel[dot]de

Publications

[ 2023 ]
[ 2022 ]
[ 2021 ]
[ 2020 ]
[ 2016 ]

2023[ to top ]

Meier, D., Ragunathan, R., Degener, S., Liehr, A., Vollmer, M., Niendorf, T., Sick, B.: Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning Scientific Reports. 13, 5410 (2023). https://doi.org/10.1038/s41598-023-31580-1.
- [ Abstract ]
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X‑ray diffraction crystallography allows non‑destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X‑ray diffraction has been highly time‑ consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep‑learning‑based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU‑based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.
@article{meier2023reconstruction, abstract = {X‑ray diffraction crystallography allows non‑destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X‑ray diffraction has been highly time‑ consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep‑learning‑based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU‑based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.}, author = {Meier, David and Ragunathan, Rishan and Degener, Sebastian and Liehr, Alexander and Vollmer, Malte and Niendorf, Thomas and Sick, Bernhard}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = 5410, publisher = {Springer Nature}, title = {Reconstruction of incomplete {X}-ray diffraction pole figures of oligocrystalline materials using deep learning}, volume = 13, year = 2023 }
%0 Journal Article %1 meier2023reconstruction %A Meier, David %A Ragunathan, Rishan %A Degener, Sebastian %A Liehr, Alexander %A Vollmer, Malte %A Niendorf, Thomas %A Sick, Bernhard %D 2023 %I Springer Nature %J Scientific Reports %N 1 %P 5410 %R 10.1038/s41598-023-31580-1 %T Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning %U https://www.nature.com/articles/s41598-023-31580-1 %V 13 %X X‑ray diffraction crystallography allows non‑destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X‑ray diffraction has been highly time‑ consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep‑learning‑based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU‑based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.

2022[ to top ]

Meier, D., Ramirez, L.V., Völker, J., Viefhaus, J., Sick, B., Hartmann, G.: Optimizing a superconducting radio-frequency gun using deep reinforcement learning Physical Review Accelerators and Beams. 25, 104604 (2022). https://doi.org/10.1103/PhysRevAccelBeams.25.104604.
- [ Abstract ]
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- [ EndNote ]
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Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.
@article{meier2022optimizing, abstract = {Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.}, author = {Meier, David and Ramirez, Luis Vera and Völker, Jens and Viefhaus, Jens and Sick, Bernhard and Hartmann, Gregor}, journal = {Physical Review Accelerators and Beams}, keywords = {SimulationTechniques}, number = 10, pages = 104604, publisher = {American Physical Society}, title = {Optimizing a superconducting radio-frequency gun using deep reinforcement learning}, volume = 25, year = 2022 }
%0 Journal Article %1 meier2022optimizing %A Meier, David %A Ramirez, Luis Vera %A Völker, Jens %A Viefhaus, Jens %A Sick, Bernhard %A Hartmann, Gregor %D 2022 %I American Physical Society %J Physical Review Accelerators and Beams %N 10 %P 104604 %R 10.1103/PhysRevAccelBeams.25.104604 %T Optimizing a superconducting radio-frequency gun using deep reinforcement learning %U https://journals.aps.org/prab/abstract/10.1103/PhysRevAccelBeams.25.104604 %V 25 %X Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.
Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses Scientific Reports. 12, 1–14 (2022). https://doi.org/10.1038/s41598-022-21646-x.
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X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.
@article{dingel2022artificial, abstract = {X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.}, author = {Dingel, Kristina and Otto, Thorsten and Marder, Lutz and Funke, Lars and Held, Arne and Savio, Sara and Hans, Andreas and Hartmann, Gregor and Meier, David and Viefhaus, Jens and Sick, Bernhard and Ehresmann, Arno and Ilchen, Markus and Helml, Wolfram}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = {1–14}, publisher = {Nature Publishing Group}, title = {Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses}, volume = 12, year = 2022 }
%0 Journal Article %1 dingel2022artificial %A Dingel, Kristina %A Otto, Thorsten %A Marder, Lutz %A Funke, Lars %A Held, Arne %A Savio, Sara %A Hans, Andreas %A Hartmann, Gregor %A Meier, David %A Viefhaus, Jens %A Sick, Bernhard %A Ehresmann, Arno %A Ilchen, Markus %A Helml, Wolfram %D 2022 %I Nature Publishing Group %J Scientific Reports %N 1 %P 1--14 %R 10.1038/s41598-022-21646-x %T Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses %U https://www.nature.com/articles/s41598-022-21646-x %V 12 %X X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.

2021[ to top ]

Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses arXiv e-prints. arXiv:2108.13979 (2021).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.
@article{dingel2021ai, abstract = {X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.}, author = {{Dingel}, Kristina and {Otto}, Thorsten and {Marder}, Lutz and {Funke}, Lars and {Held}, Arne and {Savio}, Sara and {Hans}, Andreas and {Hartmann}, Gregor and {Meier}, David and {Viefhaus}, Jens and {Sick}, Bernhard and {Ehresmann}, Arno and {Ilchen}, Markus and {Helml}, Wolfram}, journal = {arXiv e-prints}, keywords = {Accelerator}, pages = {arXiv:2108.13979}, title = {{Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses}}, year = 2021 }
%0 Journal Article %1 dingel2021ai %A Dingel, Kristina %A Otto, Thorsten %A Marder, Lutz %A Funke, Lars %A Held, Arne %A Savio, Sara %A Hans, Andreas %A Hartmann, Gregor %A Meier, David %A Viefhaus, Jens %A Sick, Bernhard %A Ehresmann, Arno %A Ilchen, Markus %A Helml, Wolfram %D 2021 %J arXiv e-prints %P arXiv:2108.13979 %T Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses %X X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.
Dingel, K., Liehr, A., Vogel, M., Degener, S., Meier, D., Niendorf, T., Ehresmann, A., Sick, B.: AI - Based On The Fly Design of Experiments in Physics and Engineering In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 150–153. IEEE (2021). https://doi.org/10.1109/ACSOS-C52956.2021.00048.
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@inproceedings{dingel2021aia, author = {Dingel, Kristina and Liehr, Alexander and Vogel, Michael and Degener, Sebastian and Meier, David and Niendorf, Thomas and Ehresmann, Arno and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {150–153}, publisher = {IEEE}, title = {AI - Based On The Fly Design of Experiments in Physics and Engineering}, year = 2021 }
%0 Conference Paper %1 dingel2021aia %A Dingel, Kristina %A Liehr, Alexander %A Vogel, Michael %A Degener, Sebastian %A Meier, David %A Niendorf, Thomas %A Ehresmann, Arno %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 150--153 %R 10.1109/ACSOS-C52956.2021.00048 %T AI - Based On The Fly Design of Experiments in Physics and Engineering

2020[ to top ]

Meier, D., Hartmann, G., Völker, J., Viefhaus, J., Sick, B.: Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm In: Advances in Computational Methods for X-Ray Optics V. bll 71–77. SPIE (2020). https://doi.org/10.1117/12.2568001.
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@inproceedings{meier2020reconstruction, author = {Meier, David and Hartmann, Gregor and Völker, Jens and Viefhaus, Jens and Sick, Bernhard}, booktitle = {Advances in Computational Methods for X-Ray Optics V}, keywords = {itegpub}, pages = {71–77}, publisher = {SPIE}, title = {Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm}, year = 2020 }
%0 Conference Paper %1 meier2020reconstruction %A Meier, David %A Hartmann, Gregor %A Völker, Jens %A Viefhaus, Jens %A Sick, Bernhard %B Advances in Computational Methods for X-Ray Optics V %D 2020 %I SPIE %P 71--77 %R 10.1117/12.2568001 %T Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm %U https://doi.org/10.1117/12.2568001

2016[ to top ]

Tomforde, S., Meier, D., Stein, A., von Mammen, S.: Distributed Resource Allocation as Co-Evolution Problem In: IEEE Congress on Evolutionary Computation (CEC). bll 1815–1822. IEEE, Vancouver, BC, Canada (2016). https://doi.org/10.1109/cec.2016.7744009.
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@inproceedings{tomforde2016distributed, address = {Vancouver, BC, Canada}, author = {Tomforde, S. and Meier, D. and Stein, A. and von Mammen, S.}, booktitle = {IEEE Congress on Evolutionary Computation (CEC)}, keywords = {imported}, pages = {1815–1822}, publisher = {IEEE}, title = {Distributed Resource Allocation as Co-Evolution Problem}, year = 2016 }
%0 Conference Paper %1 tomforde2016distributed %A Tomforde, S. %A Meier, D. %A Stein, A. %A von Mammen, S. %B IEEE Congress on Evolutionary Computation (CEC) %C Vancouver, BC, Canada %D 2016 %I IEEE %P 1815--1822 %R 10.1109/cec.2016.7744009 %T Distributed Resource Allocation as Co-Evolution Problem