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.
Edited by: Ludovic Chamoin, Andrea Manzoni, Karen Veroy-Grepl