Physics-informed neural networks for blood flow inverse problems
This work addresses clinical-relevant inverse problems in hemodynamics, where boundary information is difficult to model and high-quality measurements are hard to obtain, representing an incremental application of PINNs to a specific domain.
The paper tackled the problem of estimating reduced-order model parameters and full velocity fields from noisy 2D measurements in the ascending aorta using physics-informed neural networks (PINNs), achieving stable and accurate parameter estimations with simulated data, though velocity reconstruction depended on measurement quality and flow complexity.
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known and scatter measurements are available. This is especially useful in hemodynamics since the boundary information is often difficult to model, and high-quality blood flow measurements are generally hard to obtain. In this work, we use the PINNs methodology for estimating reduced-order model parameters and the full velocity field from scatter 2D noisy measurements in the ascending aorta. The results show stable and accurate parameter estimations when using the method with simulated data, while the velocity reconstruction shows dependence on the measurement quality and the flow pattern complexity. The method allows for solving clinical-relevant inverse problems in hemodynamics and complex coupled physical systems.