Scheduled presentations:

• A data-driven neural network trained on CFD simulation results for RANS equations for site and wind resource assessment.
  *Z. Lakdawala (Fraunhofer IWES), H. Kassem (Fraunhofer IWES), M. Nadeem (Fraunhofer IWES)
• A Three-Dimensional Machine Learning Volume Of Fluid Method
  *M. Pintore (Inria), B. Després (Sorbonne University)
• Decoding the Hidden Dynamics of Super-Arrhenius Hydrogen Diffusion in Multi-Principal Element Alloys via Machine Learning
  *F. Shuang (Delft University of Technology), P. Dey (Delft University of Technology)
• Physics-Informed Neural Networks Enhanced by Finite Element Analysis for System Identification of a Lab-Scale Nonlinear Cantilever Beam
  *E. Ghorbani (Polytechnique Montreal), M. Hamedi (Polytechnique Montreal), M. Abda (Polytechnique Montreal), Q. Dollon (Hydro-Quebec Research Institutes (IREQ)), F. Gosselin (Polytechnique Montreal)
• A machine learning interatomic potential for high entropy alloys
  *T. Li (University of Maryland)
• Student ML-Optimization Steel Structure: Offshore Wind Turbine
  J. Alves Ribeiro (Massachusetts Institute of Technology), B. Alves Ribeiro (Delft University of Technology), F. Pimenta (Faculty of Engineering, University of Porto), *S. M. O. Tavares (University of Aveiro), F. Ahmed (Massachusetts Institute of Technology)