Dieses Bild zeigt Luis Mandl

Luis Mandl

Herr M.Sc.

Kom­mis­sa­rischer Gruppenleiter Maschinelles Lernen, Wissenschaftlicher Mitarbeiter
Institut für Statik und Dynamik der Luft- und Raumfahrtkonstruktionen
[Foto: Max Kovalenko]

Kontakt

+49 711 685 69533
 +49 711 685 63706
Visitenkarte (VCF)

Pfaffenwaldring 27
70569 Stuttgart
Raum: 1.001

  1. 2025

    1. Mandl, L., Goswami, S., Lambers, L., & Ricken, T. (2025). Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning. Computer Methods in Applied Mechanics and Engineering, 434, 117586. https://doi.org/10.1016/j.cma.2024.117586

  2. 2024

    1. Arasteh-Khoshbin, O., Seyedpour, S. M., Mandl, L., Lambers, L., & Ricken, T. (2024). Comparing durability and compressive strength predictions of hyperoptimized random forests and artificial neural networks on a small dataset of concrete containing nano SiO2 and RHA. European Journal of Environmental and Civil Engineering, 1–20. https://doi.org/10.1080/19648189.2024.2393881
    2. Tautenhahn, H., Ricken, T., Dahmen, U., Mandl, L., Bütow, L., Gerhäusser, S., Lambers, L., Chen, X., Lehmann, E., Dirsch, O., & König, M. (2024). SimLivA–Modeling ischemia‐reperfusion injury in the liver: A first step towards a clinical decision support tool. GAMM-Mitteilungen. https://doi.org/10.1002/gamm.202370003

  3. 2023

    1. Mandl, L., Mielke, A., Seyedpour, S. M., & Ricken, T. (2023). Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem. Scientific Reports, 13, 15566. https://doi.org/10.1038/s41598-023-42141-x

Machine Learning Methods in Mechanics (SS22, SS23)

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