Application of Physics-Informed Neural Networks to Flows in Non-Straight Channel Geometries

Key Points

• The aim is to investigate the applicability of physics-informed neural networks (PINNs) for modeling flows in non-straight channel geometries.
• Applied PINNs to flows in converging and meandering channels.
• Compared PINNs estimations with numerical computations under the same conditions.
• Used ensemble models of PINNs to check consistency of estimations.
• Observed good agreement in depth and velocity distributions for non-linear channels.
• Identified underestimation of flow velocities in converging channels due to spectral bias.
• Noted the requirement of high frequency component learning and loss design for accurate modeling.