4 papers
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…
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…
Compact LABFM: a framework for meshless methods with spectral-like resolving power
Henry M. Broadley, Steven J. Lind, Jack R. C. King
Meshless methods are often used in numerical simulations of systems of partial differential equations (PDEs), particularly those which involve complex geometries or free surfaces.…
High-order mesh-free direct numerical simulation of lean hydrogen flames in confined geometries
H. M. Broadley, S. J. Lind, J. R. C. King
Here we perform the first analysis of high-fidelity simulations of the propagation of lean hydrogen flames through porous media, taking cylindrical arrays a representative example…