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Up to two billion times acceleration of scientific simulations with deep neural architecture search

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Feb 13 2020 Duncan Watson-Parris

Our recent #ML paper describing a new, general physics emulator (DENSE; https://arxiv.org/abs/2001.08055) just had a really nice write-up in @sciencemagazine, check it out! https://www.sciencemag.org/news/2020/02/models-galaxies-atoms-simple-ai-shortcuts-speed-simulations-billions-times https://t.co/BHfsTtCI6B
7 replies, 207 likes


Feb 15 2020 Quoc Le

Nice work on speeding up physics simulations with convolutional nets & neural architecture search.
1 replies, 168 likes


Feb 20 2020 Eirini Malliaraki

'Up to two billion times acceleration of scientific simulations with deep neural architecture search' ! https://arxiv.org/abs/2001.08055
1 replies, 42 likes


Feb 13 2020 Jan Jensen

From models of galaxies to atoms, simple AI shortcuts speed up simulations by billions of times https://www.sciencemag.org/news/2020/02/models-galaxies-atoms-simple-ai-shortcuts-speed-simulations-billions-times
0 replies, 32 likes


Feb 13 2020 News from Science

New research is showing how #AI can help accelerate simulations, like ones that model the atmosphere, by billions of times. It focuses on improving the accuracy of algorithms known as emulators. https://fcld.ly/z838rss
1 replies, 20 likes


Feb 17 2020 Daisuke Okanohara

To emulate slow simulators by NN, we can combine network architecture search to find the right model and build an accurate emulator even with small training data. Succeeded in 10 scientific simulations and achieved up to 2 billion times acceleration. http://arxiv.org/abs/2001.08055
0 replies, 19 likes


Feb 20 2020 José Luis Ricón (Artir)

Tired: Thinking about problems Wired: High-throughput search your way through problems https://twitter.com/irinimalliaraki/status/1230558582346641415 c @AdamMarblestone
1 replies, 8 likes


Feb 23 2020 Yves Hilpisch

Up to two billion times acceleration of scientific simulations with deep neural architecture search https://arxiv.org/abs/2001.08055
0 replies, 5 likes


Feb 15 2020 Brad Neuberg

Idea: train a deep net to be able to emulate a slow simulation but much faster, such as in the work at https://arxiv.org/pdf/2001.08055.pdf. Then, use this much faster deep net emulator to train a reinforcement learning algorithm such as PPO to find a policy for solving some problem.
1 replies, 3 likes


Feb 13 2020 Jan Jensen

Here's the preprint https://arxiv.org/abs/2001.08055
1 replies, 3 likes


Feb 21 2020 Chris Adams at Bosch Connected World

I don't know enough about neutral networks to know if this would actually help much with simulations for climate in practice, but the numbers are astronomical, and I learn loads from following Eirini, so....
1 replies, 2 likes


Feb 26 2020 akira

https://arxiv.org/abs/2001.08055 Up to two billion times acceleration of scientific simulations by using Neural Architecture Search. They model well with small data such as ~100 since the CNN architecture are a good prior. https://t.co/2aIgmpbuTN
0 replies, 1 likes


Mar 01 2020 Zook

i heard you like deep learning for science. so i put deep learning in your science so you can learn while you learn https://arxiv.org/abs/2001.08055
0 replies, 1 likes


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