3 papers
cond-mat.stat-mech2020
A Deep Learning Functional Estimator of Optimal Dynamics for Sampling Large Deviations
Tom H. E. Oakes, Adam Moss, Juan P. Garrahan
In stochastic systems, numerically sampling the relevant trajectories for the estimation of the large deviation statistics of time-extensive observables requires overcoming their e…
cond-mat.stat-mech2019
Trajectory phase transitions in non-interacting spin systems
Loredana M. Vasiloiu, Tom H. E. Oakes, Federico Carollo +1
We show that a collection of independent Ising spins evolving stochastically can display surprisingly large fluctuations towards ordered behaviour, as quantified by certain types o…
cond-mat.stat-mech2018
Phases of quantum dimers from ensembles of classical stochastic trajectories
Tom Oakes, Stephen Powell, Claudio Castelnovo +2
We study the connection between the phase behaviour of quantum dimers and the dynamics of classical stochastic dimers. At the so-called Rokhsar-Kivelson (RK) point a quantum dimer…