503 citations · 1.4k across the 25 of their papers we have counts for
42 papers
Learning rigid dynamics with face interaction graph networks
Kelsey R. Allen, Yulia Rubanova, Tatiana Lopez-Guevara +4
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GN…
Learned Force Fields Are Ready For Ground State Catalyst Discovery
Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose +6
We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery. Our key finding is that relaxation using forces fro…
Transframer: Arbitrary Frame Prediction with Generative Models
Charlie Nash, João Carreira, Jacob Walker +4
We present a general-purpose framework for image modelling and vision tasks based on probabilistic frame prediction. Our approach unifies a broad range of tasks, from image segment…
Rediscovering orbital mechanics with machine learning
Pablo Lemos, Niall Jeffrey, Miles Cranmer +2
We present an approach for using machine learning to automatically discover the governing equations and hidden properties of real physical systems from observations. We train a "gr…
Physical Design using Differentiable Learned Simulators
Kelsey R. Allen, Tatiana Lopez-Guevara, Kimberly Stachenfeld +4
Designing physical artifacts that serve a purpose - such as tools and other functional structures - is central to engineering as well as everyday human behavior. Though automating…
Constraint-based graph network simulator
Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff +1
In the area of physical simulations, nearly all neural-network-based methods directly predict future states from the input states. However, many traditional simulation engines inst…