24 citations · 62 across the 4 of their papers we have counts for
4 papers
Learning from learning machines: a new generation of AI technology to meet the needs of science
Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin +33
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science cre…
Varied phenomenology of models displaying dynamical large-deviation singularities
Stephen Whitelam, Daniel Jacobson
Singularities of dynamical large-deviation functions are often interpreted as the signal of a dynamical phase transition and the coexistence of distinct dynamical phases, by analog…
Evolutionary reinforcement learning of dynamical large deviations
Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn
We show how to calculate the likelihood of dynamical large deviations using evolutionary reinforcement learning. An agent, a stochastic model, propagates a continuous-time Monte Ca…
Direct evaluation of dynamical large-deviation rate functions using a variational ansatz
Daniel Jacobson, Stephen Whitelam
We describe a simple form of importance sampling designed to bound and compute large-deviation rate functions for time-extensive dynamical observables in continuous-time Markov cha…