Symposium on

Model-Consistent Data-driven Turbulence Modeling

What is this symposium about?

The past few years have witnessed great interest in data-driven turbulence modeling. While much of the initial work in this area has been devoted towards different ways of representing model discrepancies using machine learning, many recent efforts have recognized the importance of enforcing model consistency. In other words, the flow solver is integrated into the training process (i.e. inference and/or learning stage) to promote consistency between the training and prediction environments. This symposium brings together experts and participants from academia, industry and national labs who have explored different ways of approaching model consistency in machine learning augmented turbulence modeling.

Related Symposia

1. UMich/NASA Symposium on "Advances in Turbulence Modeling" (2017)
2. Symposium on "Turbulence Modeling: Roadblocks, and the Potential for Machine Learning" (2022)

Agenda

List of participants

When: June 22/23/24, 2021, 9 AM to Noon EDT

Where: Zoom

Registration: Closed

Recorded Sessions

Day 1     Day 2     Day 3

Objectives

1. Provide a picture of the state-of-the-art in data-driven turbulence modeling
2. Discuss different strategies of approaching model consistency in machine learning-augmented turbulence modeling
3. Create benchmarks and assess status of relevant tools for inference and learning

Format

• Talks
• Posters
• Discussion Sessions

Organizer

• Karthik Duraisamy (University of Michigan)

Contact

Karthik Duraisamy, kdur@umich.edu