4 papers
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.…
Improving the accuracy of meshless methods via resolving power optimisation using multiple kernels
H. Broadley, J. R. C. King, S. J. Lind
Meshless methods are commonly used to determine numerical solutions to partial differential equations (PDEs) for problems involving free surfaces and/or complex geometries, approxi…
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…