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[00817] Understanding Flood Flow Physics via Data-Informed Learning

  • Session Time & Room : 3C (Aug.23, 13:20-15:00) @E811
  • Type : Contributed Talk
  • Abstract : Modeling the dynamics of fast-moving floods has historically been an intractable problem due to the inherent complexity and multi-scale physics of the underlying processes involved. Recent advancements in physics-constrained machine learning indicate that neural networks can be used to effectively model phenomena for which physical laws are poorly understood. By combining real data and first principles, we show that we can enhance knowledge about the underlying physics of flood phenomena via the learned constitutive laws.
  • Classification : 68T07, 76T99, 86-10
  • Format : Talk at Waseda University
  • Author(s) :
    • Jonathan Thompson (University of Colorado Colorado Springs)
    • Radu Cascaval (University of Colorado Colorado Springs)