AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A physics-informed neural network framework for the Savage-Hutter model of depth-averaged granular flow is developed and validated against laboratory experiments of a cylindrical granular pile collapsing on an inclined plane, with TITAN2D providing numerical comparisons.
Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche dynamics over curved terrain remains largely unexplored. This study extends a depth-averaged PINN formulation based on the Savage-Hutter equations to an exponentially curve...
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