3 papers
physics.comp-ph2026
Learning Spectrally Optimised Mesh-Free Discretisations
Lucas Gerken Starepravo, Henry Broadley, Steven Lind +1
Numerical methods for partial differential equations (PDEs) discretise differential operators, ideally reproducing the action of the continuous operators across all wavenumbers per…
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.comp-ph2025
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…