444 citations · 1.8k across the 23 of their papers we have counts for
5 papers · 1 filter
Learned Coarse Models for Efficient Turbulence Simulation
Kimberly Stachenfeld, Drummond B. Fielding, Dmitrii Kochkov +7
Turbulence simulation with classical numerical solvers requires high-resolution grids to accurately resolve dynamics. Here we train learned simulators at low spatial and temporal r…
Learning ground states of quantum Hamiltonians with graph networks
Dmitrii Kochkov, Tobias Pfaff, Alvaro Sanchez-Gonzalez +2
Solving for the lowest energy eigenstate of the many-body Schrodinger equation is a cornerstone problem that hinders understanding of a variety of quantum phenomena. The difficulty…
ETA Prediction with Graph Neural Networks in Google Maps
Austin Derrow-Pinion, Jennifer She, David Wong +14
Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time…
Large-scale graph representation learning with very deep GNNs and self-supervision
Ravichandra Addanki, Peter W. Battaglia, David Budden +8
Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning. Many powerful solutions ha…
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt +5
In this paper we show that simple noise regularisation can be an effective way to address GNN oversmoothing. First we argue that regularisers addressing oversmoothing should both p…