SEMINAR++ part 2 - Scientific Machine Learning (Semester Programme)

Join Zoom Meeting
https://cwi-nl.zoom.us/j/83802627047?pwd=RHpJcDZvb1gxaVlSYU9tbEdsZXNFdz09

Meeting ID: 838 0262 7047
Passcode: 995879

Paola Cinnella, Professor, Institut Jean Le Rond D'Alembert at Sorbonne University.

Data-driven correction and uncertainty quantification of turbulence models using Bayesian learning and multi-model ensembles

Reynolds-averaged Navier-Stokes (RANS) models of turbulent flow are the cornerstone of flow analysis and design in fluids engineering, despite several inherent limitations that prevent them from capturing the correct physics of flows even in simple configurations. Instead, these models are developed and tuned to match certain quantities of interest to the engineer while providing reasonable performance over a wide range of flow situations.
On the other hand, the increased availability of high-fidelity data from both advanced numerical simulations and flow experiments has fostered the development of a multitude of “data-driven” turbulence model based on data-assimilation, Bayesian calibration, as well as machine learning techniques. Although these models can provide significantly better results over classical models for the narrow class of flows for which they are trained, their generalization capabilities remain far inferior to those of classical models, while the computational cost of model training and validation is significant.

In this talk I will present a methodology for developing data-driven models with improved generalization capabilities while delivering estimates of the predictive uncertainty.
This included a sparse Bayesian learning algorithm for the symbolic identification of stochastic turbulence model corrections and multi-model ensemble techniques to formulate robust predictions of unseen flow cases. The approach is demonstrated for flow cases from the NASA turbulence model testing challenge.