Physics informed neural network models blood flow in human aorta
Physics Informed Neural Network model for the dynamical study of Abdominal Aortic Aneurysm
Artificial Intelligence
Summary
Abdominal aortic aneurysms involve dangerous changes in blood flow and vessel wall stress. The authors used a special kind of AI called physics-informed neural networks (PINNs) to simulate blood moving through the aorta over time without heavy computing demands. This model estimates pressure, speed, and wall stress by combining data and physics laws. PINNs offer a cheaper and faster alternative to traditional fluid simulations while keeping results reliable.
What this means in practice
- •For cardiovascular engineers: Model complex blood flow in aneurysms quickly without meshing for design and analysis tasks.
- •For biomedical device developers: Simulate vascular dynamics efficiently to inform the design of implantable devices like stents.
Authors
Adrián Robles Arques, Martín Ruiz Fernandez, Javier Sanchis, Miguel A. Teruel, Juan Trujillo
Abstract
We present the development and application of a three-dimensional Physics-Informed Neural Network (PINN) framework for the investigation of haemodynamic behaviour in the human aorta. The model incorporates a time-resolved simulation of pulsatile blood flow over a two-minute interval, enabling the extraction of pressure and velocity fields with high temporal fidelity. The mechanical stress exerted on the aortic wall was quantified through Laplace's law, with temporal averaging applied to derive representative stress distributions. This approach circumvents the computational overhead associated with conventional computational fluid dynamics (CFD) methods by eliminating mesh generation and exploiting the automatic differentiation capabilities inherent to neural networks. The proposed methodology demonstrates that PINNs can serve as an efficient and accurate alternative for modelling complex vascular flow phenomena, offering significant advantages in scalability and computational cost reduction while maintaining physical consistency.