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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

26 replies, 2623 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, 165 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, 135 likes


👩‍💻 Paige Bailey @ 127.0.0.1 🏠: "We present a framework and model implementation that can learn to simulate a wide variety of physical domains, involving fluids, rigid solids, and deformable materials interacting with one another." 🧑‍🏫http://icml.cc/virtual/2020/poster/6849 📹http://sites.google.com/corp/view/learning-to-simulate 📄http://arxiv.org/abs/2002.09405 https://t.co/bLn6WPkS3e

2 replies, 84 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


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


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


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


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


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

0 replies, 2 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


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