Dr.-Ing. Hendrik Geisler

Dr.-Ing. Hendrik Geisler
Address
An der Universität 1
30823 Garbsen
Building
Room
303
Dr.-Ing. Hendrik Geisler
Address
An der Universität 1
30823 Garbsen
Building
Room
303

How to efficiently account for stochastic fluctuations in numerical simulations: Time-separated Stochastic Mechanics

In all physical measurements and production processes, random fluctuations play an unavoidable role. For example, it is not possible to exactly predict at which mechanical stress a material will fail or where a force acts on a component. To compensate for these uncertainties, engineers use safety factors in their calculations. This increases the safety of components and production processes, but at the cost of reduced resource efficiency.

To improve this situation, we at IKM develop the Time-separated Stochastic Mechanics, a new approach to efficiently incorporate the fluctuations of material parameters and loads efficiently in numerical simulations. The extremely low additional computational effort enables direct application to complex production processes in industry. Through the prediction of the stochastic behavior of components and processes even greater sustainability in mechanical engineering with reduced costs and increased reliability is achieved.

since  2021 Research associate at the Institute of Continuum Mechanics
2021 Master of Science "with Distinction"
2019 - 2020 Stuying at the UC Berkeley, USA
2015 - 2021 Study of Mechanical Engineering at the  TU Hamburg
  • Publications

    Showing results 1 - 7 out of 7

    A new paradigm for the efficient inclusion of stochasticity in engineering simulations: Time-separated stochastic mechanics. / Geisler, Hendrik; Erdogan, Cem; Nagel, Jan et al.
    In: Computational mechanics, 02.07.2024.

    Research output: Contribution to journalArticleResearchpeer review

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    Efficient and accurate uncertainty quantification in engineering simulations using time-separated stochastic mechanics. / Geisler, Hendrik; Junker, Philipp.
    In: Archive of applied mechanics, Vol. 94, No. 9, 09.2024, p. 2603-2617.

    Research output: Contribution to journalArticleResearchpeer review

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    Uncertainty quantification for viscoelastic composite materials using time-separated stochastic mechanics. / Geisler, Hendrik; Junker, Philipp.
    In: Probabilistic Engineering Mechanics, Vol. 76, 103618, 04.2024.

    Research output: Contribution to journalArticleResearchpeer review

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    A new paradigm for the efficient inclusion of stochasticity in engineering simulations. / Geisler, Hendrik; Erdogan, Cem; Nagel, Jan et al.
    2023.

    Research output: Working paper/PreprintPreprint

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    Time-separated stochastic mechanics for the simulation of viscoelastic structures with local random material fluctuations. / Geisler, Hendrik; Junker, Philipp.
    In: Computer Methods in Applied Mechanics and Engineering, Vol. 407, 115916, 15.03.2023.

    Research output: Contribution to journalArticleResearchpeer review

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    U p -Net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics. / Stender, Merten; Ohlsen, Jakob; Geisler, Hendrik et al.
    In: Computational mechanics, Vol. 71, No. 6, 06.2023, p. 1227–1249.

    Research output: Contribution to journalArticleResearchpeer review

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    Simulation of the dynamic behavior of viscoelastic structures with random material parameters using time-separated stochastic mechanics. / Geisler, Hendrik; Nagel, Jan; Junker, Philipp.
    In: International Journal of Solids and Structures, Vol. 259, 112012, 25.12.2022.

    Research output: Contribution to journalArticleResearchpeer review

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