• Steve Brunton, University of Washington, USA
    • Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics

  • Animashree (Anima) Anandkumar, California Institute of Technology, USA
    • Neural Operators for learning in function spaces

  • Nils Thuerey, TU Munich, Germany
    • AI techniques for differentiable and non-differentiable solvers

  • Adrian Lozano Duran, MIT, USA
    • Attention-based causality for scientific discovery in fluids

  • Noack Bernd, Shenzhen University; VTOL Manufacturing Innovation Center, Shenzhen, China
    • Aerodynamic technologies and machine learning for the aerial society ---Making drones and air taxis energy efficient and gust-safe

  • Fei Sha, Google, USA
    • Advances in Probabilistic Generative Modeling for Scientific Machine Learning

  • Alessandro Parente, Université Libre de Bruxelles, Belgium
    • Digital twins as enablers of renewable synthetic fuels in hard-to-abate industries

  • Charbel Farhat, Stanford University, USA
    • Physics-Based AI for Digital Twins of Fluid Flows

  • David Hung, Shangai Jiao Tong University, China
    • Bridging Experiments and Simulations in the Analysis of In-cylinder Air Flow Fields using Machine Learning Techniques

  • Min Xu, Shangai Jiao Tong University, China
    • TBA