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Learning to Simulate Complex Physics with Graph Networks

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Peter Battaglia: Excited to present “Learning to Simulate Complex Physics with Graph Networks”. https://arxiv.org/abs/2002.09405 Our model can generate realistic simulations, and generalizes to much larger systems and longer trajectories than its training. w/ @spectralhippo @RexYing0923 @jure https://t.co/fEimZ0LBOJ

28 replies, 2643 likes


Two Minute Papers 📜: How Well Can an AI Learn Physics? ⚛ ▶️Full video (ours): https://youtu.be/2Bw5f4vYL98 📜Source paper: https://arxiv.org/abs/2002.09405 #ai #deeplearning #science #twominutepapers #neuralnetworks #machinelearning #fluidsim https://t.co/LdB5GZDViT

0 replies, 160 likes


Two Minute Papers 📜: How Well Can an AI Learn Physics? ⚛ ▶️Full video (ours): https://youtu.be/2Bw5f4vYL98 📜Source paper: https://arxiv.org/abs/2002.09405 #ai #deeplearning #science #twominutepapers #neuralnetworks #machinelearning #fluidsim https://t.co/ooB6zBSf8g

4 replies, 127 likes


Sam Schoenholz: I am shocked / amazed by the fidelity of these learned simulations.

3 replies, 65 likes


Kyle Cranmer: 🤯👇🔥

2 replies, 40 likes


Andrew Davison: Graph networks sounds spot-on to me as the right way to simulate physics with a lot of interaction and contacts. @PeterWBattaglia you surely have to get this up and running on a graph processor; it would fly I assume. #SpatialAI

1 replies, 39 likes


Insane: 🔥 Simulating Complex Physics with Graph Networks Read More: https://arxiv.org/abs/2002.09405 Cc: @jblefevre60 @mvollmer1 @Nicochan33 @KirkDBorne @GlenGilmore @evankirstel @thomaspower @psb_dc @HeinzVHoenen @ingliguori @PawlowskiMario @MikeQuindazzi @rvp #theinsaneapp #MachineLearning https://t.co/mzt1adCbz1

0 replies, 37 likes


Shane Gu 顾世翔: Another example of extreme generalization from @PeterWBattaglia @jure et al on amortizing particle simulation using graph NNs. Train 2.5k -> test 28k. Great impactful results for computational fluid dynamics, CG and physics simulation for robotics control. https://arxiv.org/abs/2002.09405 https://t.co/Fkc0TlyS5F

0 replies, 33 likes


Xavier Bresson: Graph neural networks for computational fluid dynamics (CFD) ! CFD is applied to many industrial problems : aircraft design, aerospace, weather prediction, cardiovascular system, movie special effects, etc

0 replies, 31 likes


Shane Gu 顾世翔: An incredible feature of neural nets is that by choosing certain architectures they may exhibit extreme generalization. Similarly in sequence prediction, by choosing the right RNNs, the model may generalize extremely. https://twitter.com/TingwuWang/status/1229587903971516416

0 replies, 22 likes


Brant Robertson: A fascinating must-read for computational physicists interested in the convergence of machine learning and predictive simulation.

0 replies, 16 likes


Tone Bengtsen: And now on to protein! (Please). Can’t wait and see how ML will transform the protein simulation field. This seem like a promising step in that direction. But it does seem like there quite is some way to go for proteins

0 replies, 10 likes


Shirley Ho: Approximating simulations with #graphnetworks rock!

0 replies, 6 likes


Daisuke Okanohara: A wide range of physical simulations (fluid, rigid, solids, and deformable materials) can be emulated precisely by particle-based graph NN where each particle corresponds to a node, and neighbors are connected. https://arxiv.org/abs/2002.09405 https://drive.google.com/file/d/1Ri3RkuqyZ1xhg0QkXqzwfkxUCvn_0YKn/view?usp=sharing

0 replies, 5 likes


ML and Data Projects To Know: 📙 Learning to Simulate Complex Physics with Graph Networks Authors: Alvaro Sanchez-Gonzalez, Jonathan Godwin, @spectralhippo, @RexYing0923, @jure, @PeterWBattaglia MP4: https://drive.google.com/file/d/1Ri3RkuqyZ1xhg0QkXqzwfkxUCvn_0YKn/view Paper: https://arxiv.org/abs/2002.09405 https://t.co/XY9QGZEOww

0 replies, 5 likes


akira: https://arxiv.org/abs/2002.09405 They propose a framework for high-precision physics simulations of liquids, etc.,using Graph Neural Network(GNN). We apply GNN to nearby particles to predict the motion of a particle at the next time step. It is also robust to the choice of hyperparameters https://t.co/rqTxSdkjrS

1 replies, 5 likes


Skyzzed: oh come on, i don't even have a physics-related job yet and they're already training AI to replace me

1 replies, 2 likes


Rodolfo Rosini ☕️✨: Wow this is terribly interesting. Never imagined about using graphs to simulate physics, only higher level structures.

0 replies, 2 likes


akira: Very impressive results https://twitter.com/peterwbattaglia/status/1237425685766995974?lang=en

0 replies, 1 likes


OGAWA, Tadashi: => "Learning to Simulate Complex Physics with Graph Networks", DeepMind and Stanford, arXiv, Feb 21, 2020 https://arxiv.org/abs/2002.09405 Particles, expressed as nodes in a graph, and computes dynamics via learned message-passing MP4 https://drive.google.com/file/d/1Ri3RkuqyZ1xhg0QkXqzwfkxUCvn_0YKn/view Videos https://sites.google.com/view/learning-to-simulate/home#h.p_hjnaJ6k8y0wo https://t.co/F7xNM89ojq

1 replies, 0 likes


Content

Found on Mar 10 2020 at https://arxiv.org/pdf/2002.09405.pdf

PDF content of a computer science paper: Learning to Simulate Complex Physics with Graph Networks