2 citations · 6 across the 8 of their papers we have counts for
13 papers
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…
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…
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…
Wall Shear Stress Estimation in Abdominal Aortic Aneurysms: Towards Generalisable Neural Surrogate Models
Patryk Rygiel, Julian Suk, Christoph Brune +2
Abdominal aortic aneurysms (AAAs) are pathologic dilatations of the abdominal aorta posing a high fatality risk upon rupture. Studying AAA progression and rupture risk often involv…
Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces
Dieuwertje Alblas, Patryk Rygiel, Julian Suk +5
Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with a survival rate of only 20\%. Current clinical guidelines recomme…
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…