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Learning to Simulate Complex Physics with Graph Networks


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

26 replies, 2623 likes

Two Minute Papers 📜: How Well Can an AI Learn Physics? ⚛ ▶️Full video (ours): 📜Source paper: #ai #deeplearning #science #twominutepapers #neuralnetworks #machinelearning #fluidsim

0 replies, 165 likes

Two Minute Papers 📜: How Well Can an AI Learn Physics? ⚛ ▶️Full video (ours): 📜Source paper: #ai #deeplearning #science #twominutepapers #neuralnetworks #machinelearning #fluidsim

4 replies, 135 likes

👩‍💻 Paige Bailey @ 🏠: "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." 🧑‍🏫 📹 📄

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: Cc: @jblefevre60 @mvollmer1 @Nicochan33 @KirkDBorne @GlenGilmore @evankirstel @thomaspower @psb_dc @HeinzVHoenen @ingliguori @PawlowskiMario @MikeQuindazzi @rvp #theinsaneapp #MachineLearning

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.

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: 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

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: Paper:

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.

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

0 replies, 1 likes

OGAWA, Tadashi: => "Learning to Simulate Complex Physics with Graph Networks", DeepMind and Stanford, arXiv, Feb 21, 2020 Particles, expressed as nodes in a graph, and computes dynamics via learned message-passing MP4 Videos

1 replies, 0 likes


Found on Mar 10 2020 at

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