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Computer Science

D-Index
49
Citations
15381
World Ranking
5765
National Ranking
349

Overview

Peter W. Battaglia is affiliated with DeepMind in the United Kingdom. Their research primarily spans the field of Computer Science, with a focus on Artificial Intelligence, Atmospheric Science, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, and Astronomy and Astrophysics. Within these fields, Battaglia's work encompasses advanced topics such as Advanced Graph Neural Networks, Meteorological Phenomena and Simulations, Model Reduction and Neural Networks, Machine Learning in Materials Science, Climate Variability and Models, Tropical and Extratropical Cyclones Research, and Computational Physics and Python Applications.

Recent publications by Battaglia include:

  • Understanding the Impact of Value Selection Heuristics in Scheduling Problems (2025, arXiv (Cornell University))
  • Learning skillful medium-range global weather forecasting (2023, Science)
  • Learning to Simulate Complex Physics with Graph Networks (2020, arXiv (Cornell University))
  • Advancing mathematics by guiding human intuition with AI (2021, Nature)
  • Discovering Symbolic Models from Deep Learning with Inductive Biases (2020, arXiv (Cornell University))

Frequent collaborators in Battaglia's work include Álvaro Sánchez-González, Miles Cranmer, Shirley Ho, Stephan Hoyer, and Tobias Pfaff.

Battaglia has contributed extensively to publications in venues such as:

  • arXiv (Cornell University)
  • Nature
  • Proceedings of the National Academy of Sciences
  • Nature Human Behaviour
  • Zenodo (CERN European Organization for Nuclear Research)

Best Publications

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

  • A simple neural network module for relational reasoning

    Adam Santoro;David Raposo;David G. T. Barrett;Mateusz Malinowski

  • Interaction networks for learning about objects, relations and physics

    Peter Battaglia;Razvan Pascanu;Matthew Lai;Danilo Jimenez Rezende

  • Simulation as an engine of physical scene understanding

    Peter W. Battaglia;Jessica B. Hamrick;Joshua B. Tenenbaum

  • Learning Deep Generative Models of Graphs

    Yujia Li;Oriol Vinyals;Chris Dyer;Razvan Pascanu

  • Bayesian integration of visual and auditory signals for spatial localization

    Peter W. Battaglia;Robert A. Jacobs;Richard N. Aslin

  • Learning to Simulate Complex Physics with Graph Networks

    Alvaro Sanchez-Gonzalez;Jonathan Godwin;Tobias Pfaff;Rex Ying

  • Advancing mathematics by guiding human intuition with AI.

    Alex Davies;Petar Veličković;Lars Buesing;Sam Blackwell

  • Graph Networks as Learnable Physics Engines for Inference and Control

    Alvaro Sanchez-Gonzalez;Nicolas Heess;Jost Tobias Springenberg;Josh Merel

  • Graph neural networks in particle physics

    Jonathan Shlomi;Peter W. Battaglia;Jean-Roch Vlimant

  • Unsupervised Learning of 3D Structure from Images

    Danilo Jimenez Rezende;S. M. Ali Eslami;Shakir Mohamed;Peter W. Battaglia

  • Discovering Symbolic Models from Deep Learning with Inductive Biases

    Miles D. Cranmer;Alvaro Sanchez-Gonzalez;Peter W. Battaglia;Rui Xu

  • Imagination-Augmented Agents for Deep Reinforcement Learning

    Sébastien Racanière;Theophane Weber;David P. Reichert;Lars Buesing

  • Mind Games: Game Engines as an Architecture for Intuitive Physics

    Tomer D. Ullman;Tomer D. Ullman;Elizabeth Spelke;Peter Battaglia;Joshua B. Tenenbaum

  • Visual Interaction Networks: Learning a Physics Simulator from Video

    Nicholas Watters;Daniel Zoran;Theophane Weber;Peter W. Battaglia

  • Imagination-Augmented Agents for Deep Reinforcement Learning

    Théophane Weber;Sébastien Racanière;David P. Reichert;Lars Buesing

  • Lagrangian Neural Networks

    Miles D. Cranmer;Sam Greydanus;Stephan Hoyer;Peter W. Battaglia

  • Relational Deep Reinforcement Learning.

    Vinícius Flores Zambaldi;David Raposo;Adam Santoro;Victor Bapst

  • ETA Prediction with Graph Neural Networks in Google Maps

    Austin Derrow-Pinion;Jennifer She;David Wong;Oliver Lange

  • Learning to Simulate Complex Physics with Graph Networks

    Alvaro Sanchez;Jonathan Godwin;Tobias Pfaff;Rex

  • Deep reinforcement learning with relational inductive biases

    Vinícius Flores Zambaldi;David Raposo;Adam Santoro;Victor Bapst

  • PolyGen: An Autoregressive Generative Model of 3D Meshes

    Charlie Nash;Yaroslav Ganin;S. M. Ali Eslami;Peter Battaglia

Frequent Co-Authors

Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Matthew Botvinick
Matthew Botvinick Yale University
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Paul Schrater
Paul Schrater University of Minnesota
Daniel Kersten
Daniel Kersten University of Minnesota
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Danilo Jimenez Rezende
Danilo Jimenez Rezende DeepMind (United Kingdom)
David N. Spergel
David N. Spergel Princeton University

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