S19: Reduced order and data-driven material models

Elias Cueto (U. Zaragoza) ecueto@unizar.es
Felix Fritzen (U. Stuttgart) felix.fritzen@mechbau.uni-stuttgart.de

The need for immediacy and great degrees of interaction in many industrial and academic applications has forced the development of reduced order approaches for many problems of interest. In the last decade an impressive effort of research has been made by our community towards the successful development of such techniques. With the irruption of the industry 4.0 paradigm and the ubiquitous placement of sensors (e.g., Internet of Things, IoT), this turned out in the need of (big) data assimilation.

Going one step further, there is a growing interest on the development of techniques able to perform rapid computations supported by or based solely on data, without the need for any model or related, often costly, numerical computations.

This session is intended to be a place for the exchange of the latest results in the field of model order reduction, data-driven surrogates and hybrid techniques. Among the possible topics of interest, we cite the following (non-exhaustive) list of themes:

  • Projection-based model reduction
  • Interpolation of reduced models
  • Non-linear model reduction, manifold learning
  • Machine learning
  • Data-driven computational mechanics
  • Physics-aware machine intelligence
  • Important Dates

    • October 1, 2019
      Beginning of abstract submission

    • December 16, 2019
      Abstract submission (extended deadline)

    • January 30, 2020
      Abstract acceptance notification

    • March 15, 2020
      Last Registration for presenting authors

    • May 27-29, 2020
      EMMC 17 Conference

    organized by

    Hosted by



    Agency / Contact

    Europa i Más S.L.

    Europa i Más S.L.
    C/ Santa Pola, 13
    28008 Madrid, Spain
    phone: +34 671 916 660
    e-mail: info@emmc17.org