Mini Symposia

The ETMM-15 conference program includes regular sessions and topical mini symposia. Any author can select to submit to a mini symposium instead of the main conference track when uploading their abstract. The final decision regarding in which session your presentation will be placed will be made by the mini-symposium chairs and the organizing committee.

 


ETMM-15 will give anyone interested the opportunity to set up a mini-symposium gathering a set of talks dedicated to a relevant scientific issue. Proposals should be sent in no later than 8 January 2025. 

Guidelines and templates are available here

 

Machine learning for turbulence
Organizers: tbd

The application of machine-learning (ML) methods to fluid mechanics has experienced an exponential growth over recent years. Despite this, ML applications to fluid dynamics are still in their infancy, and the encouraging results achieved up to now have been generally restricted to academic problems characterized by simple geometries and flow physics. The availability of abundant, complete and accurate data is currently far from satisfactory in view of the deployment of ML methods to realistic flow problems. On the other hand, fine-detail understanding, accurate modeling and reliable prediction of complex flows remain significant challenges for both fundamental and applied fluid dynamics. The development of a new generation of ML-assisted methods and models for the simulation and modeling of different kinds of flows is a key enabler toward improved predictive capabilities, with impact on the design of more efficient and environmentally-friendly fluid-flow systems.

In this Symposium we aim to establish an open dialogue on these very important issues, and generate a sense of community among researchers working on data-driven methodologies for fluid mechanics, with emphasis on turbulence modeling. The development of proper benchmarking practices and configurations to define the state of the art in ML methods for fluid mechanics will also be an important aspect of this Symposium. Some of the topics included in this Symposium are:

  • Data-driven/data-augmented models for different physical phenomena in fluid dynamics, e.g., turbulence modeling.
  • ML-assisted reduced-order modeling or surrogate modeling of fluid flows, feature detection, signal processing, etc.
  • ML-based flow-control and optimization.
  • Super-resolution reconstruction of flowfields, data assimilation and sensing in experimental setups.
  • Quantification of model uncertainties, with emphasis on machine-learning-based models.