Development and Application of Machine Learning and Artificial Intelligence (ML/AI) Techniques in Hydraulics Modeling - I

Moderator(s)

• 

  Haochen Li

  Assistant Professor
  University of Tennessee, Tennessee, United States
• YK

  Yonas Kinfu

  
  Bechtel

Presentations:

• 8:15 AM – 8:30 AM Alaska Daylight Time (AKDT)

  Accelerating Computational Fluid Dynamics Simulations with Physics-Informed Neural Networks
  

  Author: Abbas Sharifi (he/him/his) – Florida International University

  Co-Author: Sumit R. Zanje (he/him/his) – Florida International University

  Co-Author: Pratik Mahyawansi – Florida International University

  Co-Author: Arturo S. Leon (he/him/his) – Florida International University
• 8:30 AM – 8:45 AM Alaska Daylight Time (AKDT)

  Inverse Source Tracking in Pipe Networks with Automatic Differentiation
  

  Author: Ahmed Abdelmeguid – University of Tennessee

  Author: Haochen Li – University of Tennessee

  Author: Qiang He – University of Tennessee
• 8:45 AM – 9:00 AM Alaska Daylight Time (AKDT)

  Hydrograd: A Differentiable 2D Shallow-water Equation Solver for Scientific Machine Learning
  

  Author: Xiaofeng Liu, PhD, PE – Pennsylvania State University
• 9:00 AM – 9:15 AM Alaska Daylight Time (AKDT)

  Understanding the Deep Learning-based PDE Solver
  

  Author: Ruo-Qian Wang – Rutgers University

  Co-Author: Ali Tavakoli – Rutgers university
• 9:15 AM – 9:30 AM Alaska Daylight Time (AKDT)

  Revolutionizing Flood and CSO Management with Advanced Machine Learning Surrogate Models
  

  Author: Siavash Hakim Elahi – Autodesk/Innovyze

  Co-Author: Sam Jamieson – Autodesk/Innovyze

  Co-Author: Samer Muhandes