Strategies for multi-case physics-informed neural networks for tube flows: a study using 2D flow scenarios.

Fluid dynamics computations for tube-like geometries are crucial in biomedical evaluations of vascular and airways fluid dynamics. Physics-Informed Neural Networks (PINNs) have emerged as a promising alternative to traditional computational fluid dynamics (CFD) methods. However, vanilla PINNs often demand longer training times than conventional CFD methods for each specific flow scenario, limiting their widespread use.

Fluid Mechanical Effects of Fetal Aortic Valvuloplasty for Cases of Critical Aortic Stenosis with Evolving Hypoplastic Left Heart Syndrome.

Fetuses with critical aortic stenosis (FAS) are at high risk of progression to HLHS by the time of birth (and are thus termed "evolving HLHS"). An in-utero catheter-based intervention, fetal aortic valvuloplasty (FAV), has shown promise as an intervention strategy to circumvent the progression, but its impact on the heart's biomechanics is not well understood. We performed patient-specific computational fluid dynamic (CFD) simulations based on 4D fetal echocardiography to assess the changes in the fluid mechanical environment in the FAS left ventricle (LV) directly before and 2 days after FAV.