Extensive deep neural networks for transferring small scale learning to large scale systems
Extensive deep neural networks for transferring small scale learning to large scale systems
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.