7 citations · 7 across the 3 of their papers we have counts for
3 papers
An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks
Joseph Webb, Sadok Jerad, Coralia Cartis
Physics-informed neural networks (PINNs) have emerged as a promising route to solve partial differential equations, yet they have struggled to reach the precision of classical solv…
Large-Angle Convergent-Beam Electron Diffraction Patterns via Conditional Generative Adversarial Networks
Joseph J. Webb, Richard Beanland, Rudolf A. Römer
We show how generative machine learning can be used for the rapid computation of strongly dynamical electron diffraction directly from crystal structures, specifically in large-ang…
Machine learning of phases and structures for model systems in physics
Djenabou Bayo, Burak Çivitcioğlu, Joseph J Webb +2
The detection of phase transitions is a fundamental challenge in condensed matter physics, traditionally addressed through analytical methods and direct numerical simulations. In r…