Alvaro Sanchez-Gonzalez | DeepAI

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∙ 08/29/2023

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

WeatherBench 2 is an update to the global, medium-range (1-14 day) weath...

0 Stephan Rasp, et al. ∙

∙ 12/24/2022

GraphCast: Learning skillful medium-range global weather forecasting

We introduce a machine-learning (ML)-based weather simulator–called "Gra...

0 Remi Lam, et al. ∙

∙ 12/07/2022

Learning rigid dynamics with face interaction graph networks

Simulating rigid collisions among arbitrary shapes is notoriously diffic...

0 Kelsey R. Allen, et al. ∙

∙ 05/31/2022

Pre-training via Denoising for Molecular Property Prediction

Many important problems involving molecular property prediction from 3D ...

0 Sheheryar Zaidi, et al. ∙

∙ 02/01/2022

Physical Design using Differentiable Learned Simulators

Designing physical artifacts that serve a purpose - such as tools and ot...

0 Kelsey R. Allen, et al. ∙

∙ 12/31/2021

Learned Coarse Models for Efficient Turbulence Simulation

Turbulence simulation with classical numerical solvers requires very hig...

5 Kimberly Stachenfeld, et al. ∙

∙ 12/16/2021

Constraint-based graph network simulator

In the rapidly advancing area of learned physical simulators, nearly all...

0 Yulia Rubanova, et al. ∙

∙ 10/12/2021

Learning ground states of quantum Hamiltonians with graph networks

Solving for the lowest energy eigenstate of the many-body Schrodinger eq...

0 Dmitrii Kochkov, et al. ∙

∙ 08/25/2021

ETA Prediction with Graph Neural Networks in Google Maps

Travel-time prediction constitutes a task of high importance in transpor...

13 Austin Derrow-Pinion, et al. ∙

∙ 07/20/2021

Large-scale graph representation learning with very deep GNNs and self-supervision

Effectively and efficiently deploying graph neural networks (GNNs) at sc...

0 Ravichandra Addanki, et al. ∙

∙ 06/15/2021

Very Deep Graph Neural Networks Via Noise Regularisation

Graph Neural Networks (GNNs) perform learned message passing over an inp...

0 Jonathan Godwin, et al. ∙

∙ 10/07/2020

Learning Mesh-Based Simulation with Graph Networks

Mesh-based simulations are central to modeling complex physical systems ...

0 Tobias Pfaff, et al. ∙

∙ 06/19/2020

Discovering Symbolic Models from Deep Learning with Inductive Biases

We develop a general approach to distill symbolic representations of a l...

12 Miles Cranmer, et al. ∙

∙ 02/21/2020

Learning to Simulate Complex Physics with Graph Networks

Here we present a general framework for learning simulation, and provide...

26 Alvaro Sanchez-Gonzalez, et al. ∙

∙ 12/05/2019

Combining Q-Learning and Search with Amortized Value Estimates

We introduce "Search with Amortized Value Estimates" (SAVE), an approach...

0 Jessica B. Hamrick, et al. ∙

∙ 10/31/2019

Object-oriented state editing for HRL

We introduce agents that use object-oriented reasoning to consider alter...

0 Victor Bapst, et al. ∙

∙ 09/27/2019

Hamiltonian Graph Networks with ODE Integrators

We introduce an approach for imposing physically informed inductive bias...

0 Alvaro Sanchez-Gonzalez, et al. ∙

∙ 04/05/2019

Structured agents for physical construction

Physical construction -- the ability to compose objects, subject to phys...

20 Victor Bapst, et al. ∙

∙ 06/04/2018

Relational inductive biases, deep learning, and graph networks

Artificial intelligence (AI) has undergone a renaissance recently, makin...

0 Peter W. Battaglia, et al. ∙

∙ 06/04/2018

Graph networks as learnable physics engines for inference and control

Understanding and interacting with everyday physical scenes requires ric...

2 Alvaro Sanchez-Gonzalez, et al. ∙

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