267 citations · 343 across the 11 of their papers we have counts for
5 papers · 1 filter
Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks
Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza +1
Wall shear stress (WSS) governs near-wall transport dynamics and is a key hemodynamic indicator in cardiovascular flows, yet remains difficult to infer accurately due to the need f…
Physics-constrained coupled neural differential equations for one dimensional blood flow modeling
Hunor Csala, Arvind Mohan, Daniel Livescu +1
Computational cardiovascular flow modeling plays a crucial role in understanding blood flow dynamics. While 3D models provide acute details, they are computationally expensive, esp…
Role of flow topology in wind-driven wildfire propagation
Siva Viknesh, Ali Tohidi, Fatemeh Afghah +2
Wildfires propagate through intricate interactions between wind, fuel, and terrain, resulting in complex behaviors that pose challenges for accurate predictions. This study investi…
Uncovering near-wall blood flow from sparse data with physics-informed neural networks
Amirhossein Arzani, Jian-Xun Wang, Roshan M. D'Souza
Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular disease, yet they are challenging to quantify with high fidelity. Patient-specific computati…
Integrating multi-fidelity blood flow data with reduced-order data assimilation
Milad Habibi, Roshan M. D'Souza, Scott T. M. Dawson +1
High-fidelity patient-specific modeling of cardiovascular flows and hemodynamics is challenging. Direct blood flow measurement inside the body with in-vivo measurement modalities s…