244 citations · 2.3k across the 68 of their papers we have counts for
5 papers · 1 filter
Demon in the machine: learning to extract work and absorb entropy from fluctuating nanosystems
Stephen Whitelam
We use Monte Carlo and genetic algorithms to train neural-network feedback-control protocols for simulated fluctuating nanosystems. These protocols convert the information obtained…
Low-dissipation self-assembly protocols of active sticky particles
Stephen Whitelam, Jeremy D. Schmit
We use neuroevolutionary learning to identify time-dependent protocols for low-dissipation self-assembly in a model of generic active particles with interactions. When the time all…
Training neural networks using Metropolis Monte Carlo and an adaptive variant
Stephen Whitelam, Viktor Selin, Ian Benlolo +2
We examine the zero-temperature Metropolis Monte Carlo algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical gro…
Cellular automata can classify data by inducing trajectory phase coexistence
Stephen Whitelam, Isaac Tamblyn
We show that cellular automata can classify data by inducing a form of dynamical phase coexistence. We use Monte Carlo methods to search for general two-dimensional deterministic a…
Learning stochastic dynamics and predicting emergent behavior using transformers
Corneel Casert, Isaac Tamblyn, Stephen Whitelam
We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of th…