Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks
Researchers have developed two inverse frameworks, a differentiable physics approach and physics-informed neural networks (PINNs), to reconstruct wall shear stress (WSS) from passive scalar observations. The study evaluated these methods on canonical and patient-specific cardiovascular flow problems. The differentiable physics framework demonstrated superior accuracy and robustness across various measurement scenarios compared to PINNs, particularly when near-wall data was limited. AI
IMPACT This research could enable more accurate hemodynamic analysis from scalar transport data, potentially improving cardiovascular diagnostics.