Minisymposium: Machine Learning in Fluid Mechanics

Presentations

• Opportunities for Machine Learning in Fluid Mechanics

  COFFEE_KLATCH · Invited

  Nov. 25, 2019, 8:00 AM – Nov. 25, 2019, 8:26 AM

  Authors

  • Michael Brenner

    • Harvard University
    • Harvard University and Google Research

  View abstract →

• Interpretable and Generalizable Machine Learning for Fluid Mechanics

  COFFEE_KLATCH · Invited

  Nov. 25, 2019, 8:26 AM – Nov. 25, 2019, 8:52 AM

  Authors

  • Steven Brunton

    • University of Washington
    • University of Washington, Seattle
    • University of Washington, department of Mechanical Engineering

  View abstract →

• Machine learning for Predictive Turbulence Modeling : A Cautiously Optimistic Perspective

  COFFEE_KLATCH · Invited

  Nov. 25, 2019, 8:52 AM – Nov. 25, 2019, 9:18 AM

  Authors

  • Karthik Duraisamy

    • University of Michigan
    • University of Michigan, Ann Arbor

  View abstract →

• Deep Reinforcement Learning for Flow Control.

  COFFEE_KLATCH · Invited

  Nov. 25, 2019, 9:18 AM – Nov. 25, 2019, 9:44 AM

  Authors

  • Petros Koumoutsakos

    • ETH Zurich

  View abstract →

• Classifying Flows using Neural Networks

  COFFEE_KLATCH · Invited

  Nov. 25, 2019, 9:44 AM – Nov. 25, 2019, 10:10 AM

  Authors

  • Eva Kanso

    • University of Southern California

  View abstract →

• Differentiable Fluid Simulations for Deep Learning

  COFFEE_KLATCH · Invited

  Nov. 25, 2019, 10:10 AM – Nov. 25, 2019, 10:36 AM

  Authors

  • Nils Thuerey

    • Technical University of Munich

  View abstract →