Graph Networks as Learnable Physics Engines for Inference and Control. - Alvaro Sanchez-Gonzalez; Nicolas Heess; Jost Tobias Springenberg; Josh Merel; Martin A. Riedmiller; Raia Hadsell; Peter W. Battaglia (ICML, 2018)

Relational inductive biases, deep learning, and graph networks. - Peter W. Battaglia; Jessica B. Hamrick; Victor Bapst; Alvaro Sanchez-Gonzalez; Vinícius Flores Zambaldi; Mateusz Malinowski; Andrea Tacchetti; David Raposo; Adam Santoro; Ryan Faulkner 001; Çaglar Gülçehre; H. Francis Song; et al. (CoRR, 2018)

Structured agents for physical construction. - Victor Bapst; Alvaro Sanchez-Gonzalez; Carl Doersch; Kimberly L. Stachenfeld; Pushmeet Kohli; Peter W. Battaglia; Jessica B. Hamrick (ICML, 2019)

CompILE: Compositional Imitation Learning and Execution. - Thomas Kipf; Yujia Li 001; Hanjun Dai; Vinícius Flores Zambaldi; Alvaro Sanchez-Gonzalez; Edward Grefenstette; Pushmeet Kohli; Peter W. Battaglia (ICML, 2019)

Hamiltonian Graph Networks with ODE Integrators. - Alvaro Sanchez-Gonzalez; Victor Bapst; Kyle Cranmer; Peter W. Battaglia (CoRR, 2019)

Object-oriented state editing for HRL. - Victor Bapst; Alvaro Sanchez-Gonzalez; Omar Shams; Kimberly L. Stachenfeld; Peter W. Battaglia; Satinder Singh 001; Jessica B. Hamrick (CoRR, 2019)

Combining Q-Learning and Search with Amortized Value Estimates. - Jessica B. Hamrick; Victor Bapst; Alvaro Sanchez-Gonzalez; Tobias Pfaff; Theophane Weber; Lars Buesing; Peter W. Battaglia (ICLR, 2020)

Learning to Simulate Complex Physics with Graph Networks. - Alvaro Sanchez-Gonzalez; Jonathan Godwin; Tobias Pfaff; Rex Ying; Jure Leskovec; Peter W. Battaglia (ICML, 2020)

Discovering Symbolic Models from Deep Learning with Inductive Biases. - Miles D. Cranmer; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Rui Xu; Kyle Cranmer; David N. Spergel; Shirley Ho (NeurIPS, 2020)

ETA Prediction with Graph Neural Networks in Google Maps. - Austin Derrow-Pinion; Jennifer She; David Wong; Oliver Lange; Todd Hester; Luis Perez; Marc Nunkesser; Seongjae Lee; Xueying Guo; Brett Wiltshire; Peter W. Battaglia; Vishal Gupta; et al. (CIKM, 2021)

Learning Mesh-Based Simulation with Graph Networks. - Tobias Pfaff; Meire Fortunato; Alvaro Sanchez-Gonzalez; Peter W. Battaglia (ICLR, 2021)

Very Deep Graph Neural Networks Via Noise Regularisation. - Jonathan Godwin; Michael Schaarschmidt; Alexander L. Gaunt; Alvaro Sanchez-Gonzalez; Yulia Rubanova; Petar Velickovic; James Kirkpatrick; Peter W. Battaglia (CoRR, 2021)

Large-scale graph representation learning with very deep GNNs and self-supervision. - Ravichandra Addanki; Peter W. Battaglia; David Budden; Andreea Deac; Jonathan Godwin; Thomas Keck; Wai Lok Sibon Li; Alvaro Sanchez-Gonzalez; Jacklynn Stott; Shantanu Thakoor; Petar Velickovic (CoRR, 2021)

Learning ground states of quantum Hamiltonians with graph networks. - Dmitrii Kochkov; Tobias Pfaff; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Bryan K. Clark (CoRR, 2021)

Learned Coarse Models for Efficient Turbulence Simulation. - Kimberly L. Stachenfeld; Drummond B. Fielding; Dmitrii Kochkov; Miles D. Cranmer; Tobias Pfaff; Jonathan Godwin; Can Cui; Shirley Ho; Peter W. Battaglia; Alvaro Sanchez-Gonzalez (CoRR, 2021)

Graph network simulators can learn discontinuous, rigid contact dynamics. - Kelsey R. Allen; Tatiana Lopez-Guevara; Yulia Rubanova; Kimberly L. Stachenfeld; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Tobias Pfaff (CoRL, 2022)

Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond. - Jonathan Godwin; Michael Schaarschmidt; Alexander L. Gaunt; Alvaro Sanchez-Gonzalez; Yulia Rubanova; Petar Velickovic; James Kirkpatrick; Peter W. Battaglia (ICLR, 2022)

Learned Simulators for Turbulence. - Kimberly L. Stachenfeld; Drummond Buschman Fielding; Dmitrii Kochkov; Miles D. Cranmer; Tobias Pfaff; Jonathan Godwin; Can Cui; Shirley Ho; Peter W. Battaglia; Alvaro Sanchez-Gonzalez (ICLR, 2022)

Constraint-based graph network simulator. - Yulia Rubanova; Alvaro Sanchez-Gonzalez; Tobias Pfaff; Peter W. Battaglia (ICML, 2022)

Inverse Design for Fluid-Structure Interactions using Graph Network Simulators. - Kelsey R. Allen; Tatiana Lopez-Guevara; Kimberly L. Stachenfeld; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Jessica B. Hamrick; Tobias Pfaff (NeurIPS, 2022)

Physical Design using Differentiable Learned Simulators. - Kelsey R. Allen; Tatiana Lopez-Guevara; Kimberly L. Stachenfeld; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Jessica B. Hamrick; Tobias Pfaff (CoRR, 2022)

TF-GNN: Graph Neural Networks in TensorFlow. - Oleksandr Ferludin; Arno Eigenwillig; Martin Blais; Dustin Zelle; Jan Pfeifer; Alvaro Sanchez-Gonzalez; Wai Lok Sibon Li; Sami Abu-El-Haija; Peter W. Battaglia; Neslihan Bulut; Jonathan Halcrow; Filipe Miguel Gonçalves de Almeida; et al. (CoRR, 2022)

GraphCast: Learning skillful medium-range global weather forecasting. - Rémi Lam; Alvaro Sanchez-Gonzalez; Matthew Willson; Peter Wirnsberger; Meire Fortunato; Alexander Pritzel; Suman V. Ravuri; Timo Ewalds; Ferran Alet; Zach Eaton-Rosen; Weihua Hu; Alexander Merose; et al. (CoRR, 2022)

Learning rigid dynamics with face interaction graph networks. - Kelsey R. Allen; Yulia Rubanova; Tatiana Lopez-Guevara; William Whitney 001; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Tobias Pfaff (ICLR, 2023)

Pre-training via Denoising for Molecular Property Prediction. - Sheheryar Zaidi; Michael Schaarschmidt; James Martens; Hyunjik Kim; Yee Whye Teh; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Razvan Pascanu; Jonathan Godwin (ICLR, 2023)

WeatherBench 2: A benchmark for the next generation of data-driven global weather models. - Stephan Rasp 001; Stephan Hoyer; Alexander Merose; Ian Langmore; Peter W. Battaglia; Tyler Russell; Alvaro Sanchez-Gonzalez; Vivian Yang; Rob Carver; Shreya Agrawal; Matthew Chantry; Zied Ben Bouallegue; et al. (CoRR, 2023)

Neural General Circulation Models. - Dmitrii Kochkov; Janni Yuval; Ian Langmore; Peter C. Norgaard; Jamie A. Smith; Griffin Mooers; James Lottes; Stephan Rasp 001; Peter D. Düben; Milan Klöwer; Sam Hatfield; Peter W. Battaglia; et al. (CoRR, 2023)

GenCast: Diffusion-based ensemble forecasting for medium-range weather. - Ilan Price; Alvaro Sanchez-Gonzalez; Ferran Alet; Timo Ewalds; Andrew El-Kadi; Jacklynn Stott; Shakir Mohamed; Peter W. Battaglia; Rémi Lam; Matthew Willson (CoRR, 2023)

Neural general circulation models for weather and climate. - Dmitrii Kochkov; Janni Yuval; Ian Langmore; Peter C. Norgaard; Jamie A. Smith; Griffin Mooers; Milan Klöwer; James Lottes; Stephan Rasp 001; Peter D. Düben; Sam Hatfield; Peter W. Battaglia; et al. (Nat., 2024)

Probabilistic weather forecasting with machine learning. - Ilan Price; Alvaro Sanchez-Gonzalez; Ferran Alet; Tom R. Andersson; Andrew El-Kadi; Dominic Masters; Timo Ewalds; Jacklynn Stott; Shakir Mohamed; Peter W. Battaglia; Rémi R. Lam; Matthew Willson (Nat., 2025)

Skillful joint probabilistic weather forecasting from marginals. - Ferran Alet; Ilan Price; Andrew El-Kadi; Dominic Masters; Stratis Markou; Tom R. Andersson; Jacklynn Stott; Rémi Lam; Matthew Willson; Alvaro Sanchez-Gonzalez; Peter W. Battaglia (CoRR, 2025)