270 citations · 594 across the 17 of their papers we have counts for
20 papers
Discovery of Unstable Singularities
Yongji Wang, Mehdi Bennani, James Martens +19
Whether singularities can form in fluids remains a foundational unanswered question in mathematics. This phenomenon occurs when solutions to governing equations, such as the 3D Eul…
Learning Perturbations for Soft-Output Linear MIMO Demappers
Daniel E. Worrall, Markus Peschl, Arash Behboodi +1
Tree-based demappers for multiple-input multiple-output (MIMO) detection such as the sphere decoder can achieve near-optimal performance but incur high computational cost due to th…
Neural Simulated Annealing
Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan
Simulated annealing (SA) is a stochastic global optimisation technique applicable to a wide range of discrete and continuous variable problems. Despite its simplicity, the developm…
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Johannes Brandstetter, Max Welling, Daniel E. Worrall
Neural networks are increasingly being used to solve partial differential equations (PDEs), replacing slower numerical solvers. However, a critical issue is that neural PDE solvers…
Message Passing Neural PDE Solvers
Johannes Brandstetter, Daniel Worrall, Max Welling
The numerical solution of partial differential equations (PDEs) is difficult, having led to a century of research so far. Recently, there have been pushes to build neural--numerica…
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
Elise van der Pol, Daniel E. Worrall, Herke van Hoof +2
This paper introduces MDP homomorphic networks for deep reinforcement learning. MDP homomorphic networks are neural networks that are equivariant under symmetries in the joint stat…