Engineering is evolving in the same way as society. Nowadays, data is earning a prominence never imagined before. In the past, in the domain of materials, processes and structures, testing machines allowed the extraction of data, which served in turn to calibrate state-of-the- art computational models.
Some calibration procedures were even integrated within testing machines. Thus, once the model was calibrated, computer simulation took place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints.
This gives rise to the family of so-called digital twins, also known as virtual and hybrid twins. Moreover, not only data can serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm.
In this collection, we will cover recent advances in the field, with a particular emphasis on grey-box approaches, i.e., those in which the laws of physics are included in the approach.
Particular topics of interest include but are not limited to:
- Scientific machine learning
- Manifold learning
- Physics-informed machine learning
- Model reductionDigital
- Hybrid twins
Lead Guest Editor
Elías Cueto, Universidad de Zaragoza
Francisco Chinesta, ENSAM ParisTech
Charbel Farhat, Stanford University
Pierre Ladeveze, ENS Paris Saclay
Francisco Javier Montans, Polytechnic University of Madrid
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