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Automated Machine Learning For Computational Mechanics (Dagstuhl Seminar 24282)

Authors: Elena Raponi, Lars Kotthoff, Hyunsun Alicia Kim, and Marius Lindauer

Published in: Dagstuhl Reports, Volume 14, Issue 7 (2025)

Abstract

Machine learning (ML) has achieved undeniable success in computational mechanics, an ever-growing discipline that impacts all areas of engineering, from structural and fluid dynamics to solid mechanics and vehicle simulation. Computational mechanics uses numerical models and time- and resource-consuming simulations to reproduce physical phenomena, usually with the goal of optimizing the parameter configuration of the model with respect to the desired properties of the system. ML algorithms enable the construction of surrogate models that approximate the outcome of the simulations, allowing faster identification of well-performing configurations. However, determining the best ML approach for a given task is not straightforward and depends on human experts. Automated machine learning (AutoML) aims to reduce the need for experts to obtain effective ML pipelines. It provides off-the-shelf solutions that can be used without prior knowledge of ML, allowing engineers to spend more time on domain-specific tasks. AutoML is underutilized in computational mechanics; there is almost no communication between the two communities, and engineers spend unnecessary effort selecting and configuring ML algorithms. Our Dagstuhl Seminar aimed to (i) raise awareness of AutoML in the computational mechanics community, (ii) discover strengths and challenges for applying AutoML in practice, and (iii) create a bilateral exchange so that researchers can mutually benefit from their complementary goals and needs.

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Elena Raponi, Lars Kotthoff, Hyunsun Alicia Kim, and Marius Lindauer. Automated Machine Learning For Computational Mechanics (Dagstuhl Seminar 24282). In Dagstuhl Reports, Volume 14, Issue 7, pp. 17-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{raponi_et_al:DagRep.14.7.17,
  author =	{Raponi, Elena and Kotthoff, Lars and Kim, Hyunsun Alicia and Lindauer, Marius},
  title =	{{Automated Machine Learning For Computational Mechanics (Dagstuhl Seminar 24282)}},
  pages =	{17--34},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2025},
  volume =	{14},
  number =	{7},
  editor =	{Raponi, Elena and Kotthoff, Lars and Kim, Hyunsun Alicia and Lindauer, Marius},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.7.17},
  URN =		{urn:nbn:de:0030-drops-229331},
  doi =		{10.4230/DagRep.14.7.17},
  annote =	{Keywords: automated algorithm design; computational mechanics; engineering applications of AI; black-box optimization; physics-informed machine learning}
}

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DOI: 10.4230/DagRep.13.8.46

Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332)

Authors: Diederick Vermetten, Martin S. Krejca, Marius Lindauer, Manuel López-Ibáñez, and Katherine M. Malan

Published in: Dagstuhl Reports, Volume 13, Issue 8 (2024)

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 23332, which focused on automated algorithm design (AAD) for optimization. AAD aims to propose good algorithms and/or parameters thereof for optimization problems in an automated fashion, instead of forcing this decision on the user. As such, AAD is applicable in a variety of domains. The seminar brought together a diverse, international set of researchers from AAD and closely related fields. Especially, we invited people from both the empirical and the theoretical domain. A main goal of the seminar was to enable vivid discussions between these two groups in order to synergize the knowledge from either domain, thus advancing the area of AAD as a whole, and to reduce the gap between theory and practice. Over the course of the seminar, a good mix of breakout sessions and talks took place, which were very well received and which we detail in this report. Efforts to synergize theory and practice bore some fruit, and other important aspects of AAD were highlighted and discussed. Overall, the seminar was a huge success.

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Diederick Vermetten, Martin S. Krejca, Marius Lindauer, Manuel López-Ibáñez, and Katherine M. Malan. Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332). In Dagstuhl Reports, Volume 13, Issue 8, pp. 46-70, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{vermetten_et_al:DagRep.13.8.46,
  author =	{Vermetten, Diederick and Krejca, Martin S. and Lindauer, Marius and L\'{o}pez-Ib\'{a}\~{n}ez, Manuel and Malan, Katherine M.},
  title =	{{Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332)}},
  pages =	{46--70},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{8},
  editor =	{Vermetten, Diederick and Krejca, Martin S. and Lindauer, Marius and L\'{o}pez-Ib\'{a}\~{n}ez, Manuel and Malan, Katherine M.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.8.46},
  URN =		{urn:nbn:de:0030-drops-198128},
  doi =		{10.4230/DagRep.13.8.46},
  annote =	{Keywords: automated algorithm design, hyper-parameter tuning, parameter control, heuristic optimization, black-box optimization}
}

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Documents authored by Lindauer, Marius

Automated Machine Learning For Computational Mechanics (Dagstuhl Seminar 24282)

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Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332)

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