• Serge Prudhomme, Polytechnique Montréal
• Ludovic Chamoin
• Jens Lang
• Fredrick Larsson
• Juan José Ródenas García

Computer simulations involve more than ever the solution of increasingly complex multi-physics, multiscale, or multi-parametrized problems on large systems. It is therefore of paramount importance to develop efficient methods to assess the accuracy of the predictions and design optimal adaptive strategies for reliable predictions and suitable decision-making, while preserving or reducing the computational effort.

The topic of error estimation and adaptation, globally referred to as model verification, goes now far beyond classical discretization error assessment and mesh refinement. It also encompasses adaptive modeling with the objective to adaptively enrich surrogate models, which, for instance, are derived from homogenization techniques, model reduction, or response surface techniques. It further includes novel topics relevant to engineering applications, such as goal-oriented methods, the control of errors due to the modeling of uncertainty or the use of inexact solvers, the control of simulation complexity in order to perform real-time simulations for optimization or control, or model adaptation from experimental data.

The objectives of the mini symposium will be to present the latest contributions to error estimation and adaptive methods, as well as new developments in all aspects of computational mechanics and applied mathematics, in relation to emerging applications in which model adaptivity and control are of primal importance.

We anticipate contributions on the following topics:
• Estimation of algebraic, discretization and modeling errors for linear, nonlinear, coupled, or time-dependent problems,
• Stability, convergence, and optimality analysis of adaptive methods,
• Goal-oriented approaches,
• Control of errors in hierarchical and multiscale modeling strategies and model reduction techniques,
• Error estimation and adaptive schemes for uncertainty quantification and optimal control on dynamical systems,
• Model enrichment from data (e.g., full-field measurements and data assimilation),
• Use of machine learning techniques for error estimation and adaptivity.