Pinning down the accuracy of physics-informed neural networks under laminar and turbulent-like aortic blood flow conditions

Our findings indicate that the accuracy of PINNs is dependent on the complexity of blood flow conditions. Using conventional PINNs architecture, the errors in trained velocity can increase substantially in the presence of turbulent-like blood flows that are typically found in various vascular pathologies.