DeepMind: The Hamiltonian Graph Network learns to simulate physics by incorporating ODE and Hamiltonian inductive biases into graph networks. https://arxiv.org/abs/1909.12790 https://t.co/TOYTEjiwCB
3 replies, 301 likes
Kyle Cranmer: Excited to announce a new paper with Alvaro Sanchez-Gonzalez, Victor Bapst, and @PeterWBattaglia (@DeepMindAI) on
"Hamiltonian Graph Networks with ODE Integrators"
Gives improvements in position & energy accuracy, and zero-shot generalization.
4 replies, 239 likes
Kyle Cranmer: Cool! @DeepMindAI is highlighting our recent paper in the context of several papers exploiting the properties of Hamiltonians. See thread, including cool new work by @DeepSpiker @irinavlh and collaborations
3 replies, 48 likes
Carlos E. Perez: This is a big step for a new kind of science. DeepMind's new paper on Hamiltonian inspired Graph Networks will lead to greater applications of generative models in Physical domains: https://twitter.com/DeepMindAI/status/1178970543682404352 .
1 replies, 12 likes
George Makrydakis: Hamiltonian Graph Networks with ODE Integrators. #ArtificialIntelligence https://arxiv.org/abs/1909.12790 https://t.co/ByVEV7dIYh
0 replies, 6 likes
Sam Greydanus: Some awesome work in the same vein as our HNN paper
0 replies, 5 likes
Montréal.IA: Hamiltonian Graph Networks with ODE Integrators
Sanchez-Gonzalez et al.: https://arxiv.org/abs/1909.12790
#ArtificialIntelligence #Hamiltonian #GraphNetworks https://t.co/afbspWUr2y
0 replies, 4 likes
Kyle Cranmer: @alfcnz @samgreydanus @jasonyo 👋 @samgreydanus see you next week too.
For this reading the thread, here is something related
0 replies, 3 likes
OGAWA, Tadashi: =>
"Learning Structured Models of Physics", P. Battaglia, DeepMind, Interpretable Learning in Physical Sci, IPAM, Oct 16, 2019
Hamiltonian Graph Networks with ODE Integrators https://arxiv.org/abs/1909.12790
0 replies, 1 likes
Found on Oct 01 2019 at https://arxiv.org/pdf/1909.12790.pdf