Extensive deep neural networks for transferring small scale learning to large scale systems
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- Kyle Mills
- University of Ontario Institute of Technology
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- Kevin Ryczko
- University of Ottawa
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- Iryna Luchak
- University of British Columbia
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- Adam Domurad
- University of Waterloo
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- Chris Beeler
- University of Ontario Institute of Technology
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- Isaac Tamblyn
- University of Ontario Institute of Technology
説明
<p>We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with <graphic xmlns:xlink="http://www.w3.org/1999/xlink" id="ugt1" xlink:href="http://pubs.rsc.org/SC/2019/c8sc04578j/c8sc04578j-t1..gif" /> scaling.</p>
収録刊行物
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- Chemical Science
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Chemical Science 10 (15), 4129-4140, 2019
Royal Society of Chemistry (RSC)
