175 citations · 362 across the 9 of their papers we have counts for
5 papers · 1 filter
Phase space sampling and operator confidence with generative adversarial networks
Kyle Mills, Isaac Tamblyn
We demonstrate that a generative adversarial network can be trained to produce Ising model configurations in distinct regions of phase space. In training a generative adversarial n…
Extensive deep neural networks for transferring small scale learning to large scale systems
Kyle Mills, Kevin Ryczko, Iryna Luchak +3
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…
Deep neural networks for direct, featureless learning through observation: the case of 2d spin models
Kyle Mills, Isaac Tamblyn
We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4x4 Ising model. Using its success at this task, we motivate t…
Convolutional neural networks for atomistic systems
Kevin Ryczko, Kyle Mills, Iryna Luchak +2
We introduce a new method, called CNNAS (convolutional neural networks for atomistic systems), for calculating the total energy of atomic systems which rivals the computational cos…
Deep learning and the Schrödinger equation
Kyle Mills, Michael Spanner, Isaac Tamblyn
We have trained a deep (convolutional) neural network to predict the ground-state energy of an electron in four classes of confining two-dimensional electrostatic potentials. On ra…