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Newton vs the machine: solving the chaotic three-body problem using deep neural networks

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Eric Topol: In 1687 Sir Issac Newton posited the three-body problem in Principia. 332 years later it was solved with a deep neural network, and 100 million times faster https://www.technologyreview.com/s/614597/a-neural-net-solves-the-three-body-problem-100-million-times-faster/ @techreview #AI https://arxiv.org/pdf/1910.07291.pdf Philip Breen @EdinburghUni cc @stevenstrogatz for input https://t.co/vYC3plj9Jp

5 replies, 220 likes


Kovas Boguta: "Complex systems winter" is coming to a close. Newton vs the machine: solving the chaotic three-body problem using deep neural networks https://arxiv.org/abs/1910.07291

1 replies, 18 likes


Daisuke Okanohara: The three-body problem has no analytic solution in general, and its simulation cost tends to be very large due to its chaotic nature. Surprisingly, NN can approximate the solution at a fixed cost and up to 100 million times faster than existing solvers. https://arxiv.org/abs/1910.07291

0 replies, 18 likes


Joshua Bloom: Funny figure aside, I enjoyed this paper out today: "Newton vs the machine: solving the chaotic three-body problem using deep neural networks" by Breen et al. Great example of cross-disciplinary work between N-body astronomers and computational folks https://arxiv.org/abs/1910.07291 https://t.co/Q5JyEsOnxl

1 replies, 9 likes


Carlos E. Perez 🧢: Deep Learning solves the 3-body problem! If you aren't convinced yet, then you are beyond redemption!! https://arxiv.org/abs/1910.07291 #ai #deeplearning

1 replies, 8 likes


Umut Eser: Super sensitivity to micro states is often prohibitive for bottom up knowledge-driven simulations. But thanks to emergent behaviours, there are also a huge ton of invariences which can be modeled with data driven inferences (e.g. Deep Learning). 1/2

1 replies, 5 likes


Sibesh Kar: Misleading. Any solution that involves numerical methods always requires the same amount of compute, regardless of whether that compute is used to calculate approximate trajectories, or train neural networks to approximate trajectories. Can't escape limits of information theory

0 replies, 4 likes


Marius: Newton vs the machine: solving the chaotic three-body problem using deep neural networks. Find out more here: https://arxiv.org/pdf/1910.07291.pdf @DFKI @acatech_de @Stanford @damianborth #MachineLearning #NeuralNetworks @India_AI_brains @AINewsFeed https://t.co/DKuS2TErBh

0 replies, 3 likes


Javier Abellán: In the paper "Newton vs the machine: solving the 3-body problem using DL" they compare Newton laws with a 10-layer neural network. How awesome is that? Their solution could be another chapter of the Liu Cixin novel 😁 Paper link: https://arxiv.org/abs/1910.07291 https://t.co/MXmRdA4lUC

0 replies, 3 likes


philipbreen: Check out my latest article: Newton vs the machine: using deep neural networks to tackle a 300-year-old problem https://www.linkedin.com/pulse/newton-vs-machine-using-deep-neural-networks-tackle-problem-breen via @LinkedIn or the preprint on https://arxiv.org/abs/1910.07291 @arxiv

0 replies, 3 likes


Carlos Ciller: Early morning #MachineLearning digest - Newton vs The Machine: Solving The Chaotic Three-Body Problem Using Deep Neural Networks via @techreview #breakfast Article https://www.technologyreview.com/s/614597/a-neural-net-solves-the-three-body-problem-100-million-times-faster/ #Arxiv Paper http://arxiv.org/abs/1910.07291

0 replies, 2 likes


Machine Learning: Science and Technology: Newton vs the machine: solving the chaotic three-body problem using deep neural networks at https://arxiv.org/abs/1910.07291 #MachineLearning #astrophysics

0 replies, 2 likes


Machine Learning: Newton vs the machine: solving the chaotic three-body problem using deep neural networks. http://arxiv.org/abs/1910.07291

0 replies, 2 likes


Pascal Kwanten | Less Artificial Very Intelligist: https://arxiv.org/abs/1910.07291

0 replies, 2 likes


Larry Hunter: Cool result: "A deep neural network ... provides accurate solutions at fixed computational cost up to 100 million times faster than state-of-the-art ... numerical solvers, enabling fast and scalable simulations of many-body systems.” https://arxiv.org/pdf/1910.07291.pdf

0 replies, 2 likes


Jake Vikoren: @GaryMarcus @Montreal_AI A recent paper (https://arxiv.org/abs/1910.07291) uses neural nets to efficiently model chaotic systems. How you see this affecting our ability to simulate complex systems? Do you think deep learning can help us model quantum systems more accurately/efficiently?

0 replies, 1 likes


DLビギナ: https://twitter.com/AkiraTOSEI/status/1189882225451397120?16814

0 replies, 1 likes


Content

Found on Oct 26 2019 at https://arxiv.org/pdf/1910.07291.pdf

PDF content of a computer science paper: Newton vs the machine: solving the chaotic three-body problem using deep neural networks