3 papers
physics.comp-ph2026
Exploring Neural Network Surrogates for High-Order Mesh-Free Interpolants
Lucas Gerken Starepravo, Georgios Fourtakas, Steven Lind +2
Mesh-free numerical methods offer flexibility in the discretisation of complex geometries, showing significant potential for problems where mesh-based methods struggle. Although hi…
cs.LG2026
Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networks
Lucas Gerken Starepravo, Georgios Fourtakas, Steven Lind +3
Mesh-free numerical methods provide flexible discretisations for complex geometries; however, classical meshless discrete differential operators typically trade low computational c…
physics.med-ph2025
An in silico approach to analyse the influence of carotid haemodynamics on cardiovascular events using 3D tomographic ultrasound and computational fluid dynamics
Sampad Sengupta, Emily Manchester, Jie Wang +3
Analysing the haemodynamics of flow in carotid artery disease serves as a means to better understand the development and progression of associated complex diseases. Carotid artery…