10 papers
Inpainting physics: self-supervised learning for context-driven fluid simulation
Jonas Weidner, Yeray Martin-Ruisanchez, Daniel Rueckert +2
Neural surrogate models for computational fluid dynamics (CFD) are typically trained as forward operators that map explicit problem specifications, such as geometry and boundary co…
Symmetry in the Wild: The Role of Equivariance in Neural Fluid Surrogates
Patryk Rygiel, Julian Suk, Kak Khee Yeung +2
Neural surrogates enable orders-of-magnitude acceleration of computational fluid dynamics (CFD) simulations, with the potential to transform engineering and healthcare workflows. N…
Physics-informed graph neural networks for flow field estimation in carotid arteries
Julian Suk, Dieuwertje Alblas, Barbara A. Hutten +4
Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be…
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation
Patryk Rygiel, Julian Suk, Kak Khee Yeung +2
Hemodynamic parameters such as pressure and wall shear stress play an important role in diagnosis, prognosis, and treatment planning in cardiovascular diseases. These parameters ca…
GReAT: leveraging geometric artery data to improve wall shear stress assessment
Julian Suk, Jolanda J. Wentzel, Patryk Rygiel +3
Leveraging big data for patient care is promising in many medical fields such as cardiovascular health. For example, hemodynamic biomarkers like wall shear stress could be assessed…
Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior
Julian Suk, Guido Nannini, Patryk Rygiel +4
Cardiovascular hemodynamic fields provide valuable medical decision markers for coronary artery disease. Computational fluid dynamics (CFD) is the gold standard for accurate, non-i…