Video URL
https://pirsa.org/18040099Deep models - hard problems made easy with deep learning
APA
Tamblyn, I. (2018). Deep models - hard problems made easy with deep learning. Perimeter Institute for Theoretical Physics. https://pirsa.org/18040099
MLA
Tamblyn, Isaac. Deep models - hard problems made easy with deep learning. Perimeter Institute for Theoretical Physics, Apr. 16, 2018, https://pirsa.org/18040099
BibTex
@misc{ scivideos_PIRSA:18040099,
doi = {10.48660/18040099},
url = {https://pirsa.org/18040099},
author = {Tamblyn, Isaac},
keywords = {Quantum Matter},
language = {en},
title = {Deep models - hard problems made easy with deep learning},
publisher = {Perimeter Institute for Theoretical Physics},
year = {2018},
month = {apr},
note = {PIRSA:18040099 see, \url{https://scivideos.org/index.php/pirsa/18040099}}
}
Isaac Tamblyn University of Ottawa
Abstract
Recently, we have shown that deep neural networks can be used to solve the Schrödinger Equation (https://journals.aps.org/pra/abstract/10.1103/PhysRevA.96.042113), classical spin models (https://doi.org/10.1103/PhysRevE.97.032119), and 2d-materials such as graphene and boron-nitride (https://doi.org/10.1016/j.commatsci.2018.03.005).
I will argue that these "deep models" can be used to simulate and understand matter in a way which was not previously possible. Specifically, I will show how our recently reported extensive deep neural networks (https://arxiv.org/abs/1708.06686) can be used to infer the properties of meso-scale materials based on training data generated from much smaller structural motifs (evaluated using electronic structure methods such as density functional theory). Extensive deep neural networks scale as O(N) and can be efficiently evaluated in parallel using petascale computational platforms.