DDF: Data, damage and fracture
Minisymposium organized by
- O. Allix, ENS Paris-Saclay, France
- P. Carrara, ETH Zurich, Switzerland
Thanks to their ability to merge theoretical, computational and experimental aspects, data-driven, machine learning and artificial intelligence approaches are boosting many aspects of mechanics. To date, however, little attention has been paid to the fracture and damage constitutive behaviors despite their practical relevance. This is not expected to last long and the first results in this direction already confirm that synergistic approaches coupling data science and mechanics will be crucial for the progress of the field, allowing to introduce new paradigms and to unlock a deeper understanding of the processes.
Along with the advantages come the challenges, such as the mitigation of the data dependency through the introduction of physical knowledge within the learning process, the need to improve the techniques to generate data (experimentally or in-silico) and the development of frameworks that allow the coupling between experimental and computational approaches.
In this spirit, this minisymposium is seen as a forum to stimulate discussion and gather ground-breaking contributions in the field of data-driven and/or deep-learning damage and fracture mechanics as well as to promote the exchange of ideas within the community.
The topics of interest include, but are not limited, to:
- Which data are useful to describe fracture and damage? – How to use and where to find them?
- Data-driven fracture and damage mechanics.
- Model order reduction and surrogate modeling of damage and fracture processes.
- Machine learning methods applied to experimental testing and diagnostic in the context of damage and fracture mechanics.
- Data-driven and deep-learning techniques in computational damage and fracture mechanics.
- Automated discovery of evolution laws or material parameters for damage or fracture.
- Approaches integrating experimental data and numerical methods.