Uncertainty Quantification and Data Assimilation in Fluid Dynamics

Purpose and focus of SIG

Advances in fluid mechanics in the framework of Industry 4.0 aim at the development of predictive models to obtain fast and accurate predictions of complex flows. However, several scientific barriers must be overcome in order to achieve these ambitious objectives. Among these difficulties, accounting for uncertainties naturally affecting realistic systems is essential, because of their importance in the instantaneous organization of the flow and in the emergence of extreme events. These rare events are usually responsible for important local flow effects and their correct prediction is fundamental.
The objectives of the SIG45 target advancement of fundamental and applied techniques dealing with the estimation of uncertainties and their control. On the one hand, Uncertainty Quantification (UQ) techniques can be used to assess their impact on the flow features. On the other hand, Data Assimilation (DA) techniques can exploit the uncertainty affecting several sources of information in order to obtain advanced state estimation. This state can be used to optimize and train underlying models for fast prediction and control.

The activities of the SIG will focus on four main topics of recent interest in the community:

1)Usage of sparse information for the observed phenomena at play, which may set the stage for small data problems.

2)Development and application of multifidelity and multilevel techniques in UQ and DA algorithms

3)Methods for dynamical systems and strongly time-dependent problems (e.g. highly unsteady flows and extreme events)

4​)Development of digitals twins / online applications

It is the long term objective of this SIG to coordinate and encourage joint research efforts between the SIG's industrial and academic members (this may include the definition of joint test cases, organization of workshops, etc.) on uncertainty quantification and data assimilation. In this way, we hope to encourage interaction between industry and academic research,  stimulate the transfer of academic research to industry, and identify new challenges for research based on the needs encountered in industrial applications.