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Dagstuhl Seminar 24282

Automated Machine Learning for Computational Mechanics

( Jul 07 – Jul 12, 2024 )

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Machine learning (ML) has revolutionized Computational Mechanics, impacting all areas of engineering, from structural and fluid dynamics to solid mechanics and vehicle simulation. Computational mechanics employs numerical models and time- and resource-consuming simulations to replicate physical phenomena, usually with the final goal of optimizing the parameter configuration of the model with respect to desired purposes and functionalities of the system. In this context, ML algorithms can play a pivotal role by facilitating the creation of models that closely approximate the outcomes of simulations, expediting the identification of high-performing configurations. Nonetheless, the task of determining the most suitable design approach for a given undertaking is far from straightforward and heavily relies on human expertise.

Automated machine learning (AutoML) aims to reduce the need for experts to obtain effective ML pipelines. It offers off-the-shelf solutions that can be applied without prior knowledge in ML, allowing engineers to dedicate more time to domain-specific tasks. Regrettably, 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.

In light of this, the current Dagstuhl Seminar is dedicated to fostering cross-pollination between the fields of AutoML and Computational Mechanics. It aims to convene a curated selection of international experts, each hailing from diverse backgrounds and various application domains, encompassing computer science, machine learning, engineering, mathematics, operations research, and industrial applications. The overarching objectives are:

  1. Raising awareness of AutoML in the computational mechanics community.
  2. Unearthing strengths and challenges of applying AutoML in practice.
  3. Fostering a bilateral exchange that allows researchers to mutually benefit from their complementary goals and needs.
Discussions within the seminar will revolve primarily around the following key inquiries:
  • What exactly does AutoML entail, and to what extent is it interpretable?
  • In what ways can AutoML tools empower the Computational Mechanics community?
  • What aspects of computational mechanics benchmarks currently lack robust support from AutoML, and what limitations of AutoML tools hinder their application in Computational Mechanics?
  • How can applications in computational mechanics inform the research directions of AutoML?

We firmly believe that there exists significant potential for mutual support between the AutoML and Computational Mechanics communities, a potential that can be fully realized through the exchange of ideas. We are confident that these exchanges will culminate in collaborations that extend beyond the scope of the seminar.

Participants will have ample opportunities to engage in meaningful discussions through breakout groups, speed dating sessions, round tables, and more. Additionally, presentations by esteemed researchers from both the research domains are planned to delve into specific topics and ensure that all attendees can actively participate in the full spectrum of offerings. This seminar not only promises to be a platform for forging new connections but also for shaping the future landscape of the intersection of AutoML and Computational Mechanics.

Copyright Hyunsun Alice Kim, Lars Kotthoff, Marius Lindauer, and Elena Raponi


  • Artificial Intelligence
  • Computational Engineering / Finance / and Science
  • Machine Learning

  • automated algorithm design
  • computational mechanics
  • engineering applications of AI
  • black-box optimization
  • physics-informed machine learning