- Ludovic Chamoin (ENS Paris-Saclay, Gif-sur-Yvette, France)
- Andrea Manzoni (Politecnio di Milano, Milan, Italy)
- Karen Veroy-Grepl (RWTH Aachen University, Aachen, Germany)
The use of experimental data in association with simulation models has become an active research topic. Indeed, new experimental facilities (such as digital image/volume correlation (DIC/DVC)) now enable to collect a large and diversified amount of data, and these may be used to identify and validate complex models, or to enhance predictions made by simulations tools. Furthermore, data and models are more and more intertwined to improve knowledge in applications dealing with structural health monitoring and control for instance, with potential real-time dialogue between simulators and connected physical systems (e.g., the DDDAS concept).
However, many challenges dealing with data filtering, computational cost, or
numerical robustness need to be addressed in order to incorporate data efficiently.
The goal of this special issue is to present, in both deterministic and stochastic (Bayesian) contexts, recent fundamental advances in data assimilation and inverse methods with regards to innovative and powerful numerical approaches which emerged during the last years.
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.