ID: PhysRevE-97-032119
Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models
Kyle Mills and Isaac Tamblyn
Phys. Rev. E
97,
032119 (Mar 2018)
DOI
arXiv
PDF
ID: PhysRevE-97-032119
ID: PhysRevE-97-032119
ID: Ryczko2018134
Convolutional neural networks for atomistic systems
Kevin Ryczko, Kyle Mills, Iryna Luchak, Christa Homenick, and Isaac Tamblyn
Computational Materials Science
149,
134 - 142 (Mar 2018)
DOI
arXiv
PDF
ID: Ryczko2018134
ID: Ryczko2018134
ID: PORTMAN2017871
Sampling algorithms for validation of supervised learning models for Ising-like systems
Nataliya Portman and Isaac Tamblyn
Journal of Computational Physics
350,
871 - 890 (Dec 2017)
DOI
arXiv
PDF
ID: PORTMAN2017871
ID: PORTMAN2017871
ID: PhysRevA-96-042113
Deep learning and the Schrödinger equation
Kyle Mills, Michael Spanner, and Isaac Tamblyn
Phys. Rev. A
96,
042113 (Oct 2017)
DOI
arXiv
PDF
ID: PhysRevA-96-042113
ID: PhysRevA-96-042113