- Elias Cueto (University of Zaragoza, Zaragoza, Spain)
- Francisco Chinesta (PIMM Laboratory, Arts et Métiers Institute of Technology, HESAM Université, Paris, France)
- Charbel Farhat (Stanford University, California, USA)
- Pierre Ladeveze (Université Paris-Saclay, Gif-sur-Yvette, France)
- Francisco Javier Montans (Universidad Politécnica de Madrid, Madrid, Spain)
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.
Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies, including those pertaining to Collections. Articles will be added to the Collection as they are published.