Alvaro Sanchez-Gonzalez

According to our database, Alvaro Sanchez-Gonzalez authored at least 30 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

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., January, 2025

2024

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, Peter D. Düben, Sam Hatfield, Peter W. Battaglia, Alvaro Sanchez-Gonzalez, Matthew Willson, Michael P. Brenner, Stephan Hoyer

Nat., August, 2024

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.

Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter C. Norgaard, Jamie A. Smith, Griffin Mooers, James Lottes, Stephan Rasp, Peter D. Düben, Milan Klöwer, Sam Hatfield, Peter W. Battaglia, Alvaro Sanchez-Gonzalez, Matthew Willson, Michael P. Brenner, Stephan Hoyer

CoRR, 2023

WeatherBench 2: A benchmark for the next generation of data-driven global weather models.

Stephan Rasp, 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, Peter Dueben, Carla Bromberg, Jared Sisk, Luke Barrington, Aaron Bell, Fei Sha

CoRR, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning rigid dynamics with face interaction graph networks.

Kelsey R. Allen, Yulia Rubanova, Tatiana Lopez-Guevara, William Whitney, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Tobias Pfaff

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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, Stephan Hoyer, George Holland, Jacklynn Stott, Oriol Vinyals, Shakir Mohamed, Peter W. Battaglia

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, Silvio Lattanzi, André Linhares, Brandon A. Mayer, Vahab S. Mirrokni, John Palowitch, Mihir Paradkar, Jennifer She, Anton Tsitsulin, Kevin Villela, Lisa Wang, David Wong, Bryan Perozzi

CoRR, 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

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Constraint-based graph network simulator.

Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Peter W. Battaglia

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Tenth International Conference on Learning Representations, 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

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

Proceedings of the Conference on Robot Learning, 2022

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

Learning ground states of quantum Hamiltonians with graph networks.

Dmitrii Kochkov, Tobias Pfaff, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Bryan K. Clark

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

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

Learning Mesh-Based Simulation with Graph Networks.

Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, Peter W. Battaglia

Proceedings of the 9th International Conference on Learning Representations, 2021

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, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, Petar Velickovic

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning to Simulate Complex Physics with Graph Networks.

Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia

Proceedings of the 37th International Conference on Machine Learning, 2020

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

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Object-oriented state editing for HRL.

Victor Bapst, Alvaro Sanchez-Gonzalez, Omar Shams, Kimberly L. Stachenfeld, Peter W. Battaglia, Satinder Singh, Jessica B. Hamrick

CoRR, 2019

Hamiltonian Graph Networks with ODE Integrators.

Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, Peter W. Battaglia

CoRR, 2019

CompILE: Compositional Imitation Learning and Execution.

Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia

Proceedings of the 36th International Conference on Machine Learning, 2019

Structured agents for physical construction.

Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick

Proceedings of the 36th International Conference on Machine Learning, 2019

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, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew M. Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu

CoRR, 2018

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

Proceedings of the 35th International Conference on Machine Learning, 2018