35 citations · 36 across the 6 of their papers we have counts for
9 papers
CARS: a centerline-anchored attention surrogate for coronary blood flow on skeleton-generated lumen geometries
Reza Akbarian Bafghi, Sukirt Thakur, Maziar Raissi
When the parameter of a PDE surrogate is the flow domain itself, the key/value set of an attention model must encode that domain. We present CARS (Coronary Artery Rapid Surrogate),…
Are Learned Vascular Tree Generators Better Than Conditional Resampling?
Reza Akbarian Bafghi, Sukirt Thakur, Maziar Raissi
Fidelity scores compare generated vascular trees with held-out real trees, so by themselves they cannot detect reuse of training data. A training-free conditional resampler shows t…
From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries
Reza Akbarian Bafghi, Sukirt Thakur, Maziar Raissi
Accurate and rapid estimation of hemodynamic metrics, such as pressure and wall shear stress (WSS), is important for assessing the severity of Coronary Artery Disease (CAD). Existi…
PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics
Sukirt Thakur, Marcus Roper, Yang Zhou +8
More than 10 million coronary angiograms are performed globally each year, providing a gold standard for detecting obstructive coronary artery disease. Yet, no obstructive lesions…
ELPINN: Eulerian Lagrangian Physics-Informed Neural Network
Sukirt Thakur, Maziar Raissi
Physics-Informed Neural Networks (PINNs) have gained widespread popularity for solving inverse and forward problems across a range of scientific and engineering domains. However, m…
Physics-Informed Neural Network based inverse framework for time-fractional differential equations for rheology
Sukirt Thakur, Harsa Mitra, Arezoo M. Ardekani
Time-fractional differential equations offer a robust framework for capturing intricate phenomena characterized by memory effects, particularly in fields like biotransport and rheo…
Inverse resolution of spatially varying diffusion coefficient using Physics-Informed neural networks
Sukirt Thakur, Ehsan Esmaili, Sarah Libring +2
Resolving the diffusion coefficient is a key element in many biological and engineering systems, including pharmacological drug transport and fluid mechanics analyses. Additionally…
Temporal Consistency Loss for Physics-Informed Neural Networks
Sukirt Thakur, Maziar Raissi, Harsa Mitra +1
Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations in a forward and inverse manner using deep neural networks. However, training…
ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection
Sukirt Thakur, Maziar Raissi, Arezoo M. Ardekani
Viscoelastic fluids are a class of fluids that exhibit both viscous and elastic nature. Modelling such fluids requires constitutive equations for the stress, and choosing the most…