ADMOS 2023

About ADMOS 2023

Numerical modeling and simulation are vastly used for engineering applications, and as complement to theoretical and experimental developments boosting progress in scientific knowledge. Due to their versatility, numerical methods are successively and increasingly replacing physical tests in industrial design processes. They are also an important item in the toolbox of scientific researchers, both in fundamental and applied disciplines. There are two competing trends in the development of numerical methods. While the models become more elaborate, e.g. by considering more detailed and/or coupled physical phenomena on one or several time- and length-scales, there is a strong drive towards obtaining fast predictions with applications to, e.g., real-time decision tools. Other applications requiring fast numerical simulations while controlling accuracy are large-scale optimization problems and statistical analysis, where a large number of simulations are required to evaluate the parameter space.

Adaptive Modeling and Simulation is the field of balancing predictive accuracy and computational effort. It addresses the competing equilibrium between guaranteeing the credibility of the result, and an affordable computation. In addition to developing fast simulation tools, this concerns the important task of assessing the quality of a numerical prediction and guide the refinement of the numerical procedure in the best way. While a certain maturity has been reached within adaptive finite element discretization, there is still an ever-growing number of applications and problem formulations where verification tools and procedures for model adaptation have plenty of room for improvement.

The XI International Conference on Adaptive Modeling and Simulation (ADMOS 2023) targets this important field concerning the development of fast simulation tools, verification procedures, and adaptation of models and discretization schemes. The topics of the conference include, but are not restricted to

  • Error estimation (due to discretization and/or modeling)
  • Discretization techniques and high-fidelity schemes
  • Reduced-order models
  • Multi-scale and multi-physics modeling
  • Machine learning assisted computations
  • Optimization and inverse problems
  • Uncertainty Quantification, and its connection with accuracy


Fredrik Larsson & Pedro Díez
ADMOS 2023 Organizers