[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.
