573 citations · 5.3k across the 57 of their papers we have counts for
9 papers · 1 filter
Graph Networks with Spectral Message Passing
Kimberly Stachenfeld, Jonathan Godwin, Peter Battaglia
Graph Neural Networks (GNNs) are the subject of intense focus by the machine learning community for problems involving relational reasoning. GNNs can be broadly divided into spatia…
Learning Mesh-Based Simulation with Graph Networks
Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez +1
Mesh-based simulations are central to modeling complex physical systems in many disciplines across science and engineering. Mesh representations support powerful numerical integrat…
Predicting the long-term stability of compact multiplanet systems
Daniel Tamayo, Miles Cranmer, Samuel Hadden +11
We combine analytical understanding of resonant dynamics in two-planet systems with machine learning techniques to train a model capable of robustly classifying stability in compac…
Graph Neural Networks in Particle Physics
Jonathan Shlomi, Peter Battaglia, Jean-Roch Vlimant
Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs---set…
Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia +4
We develop a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The tech…
Lagrangian Neural Networks
Miles Cranmer, Sam Greydanus, Stephan Hoyer +3
Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet…
PolyGen: An Autoregressive Generative Model of 3D Meshes
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami +1
Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development. Existing learning-based approache…
Learning to Simulate Complex Physics with Graph Networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff +3
Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and…
Combining Q-Learning and Search with Amortized Value Estimates
Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez +4
We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned pri…