24 citations · 34 across the 9 of their papers we have counts for
9 papers
Work as a function of protocol duration for the efficient erasure of an underdamped memory: isothermal to adiabatic transition
Nicolas Barros, Stephen Whitelam, Sergio Ciliberto +1
We use evolutionary reinforcement learning to determine efficient time-dependent erasure protocols for an underdamped cantilever moving in a double-well potential, an experimental…
A neural-network Maxwell's demon learns cold damping for work extraction
Stephen Whitelam, Sergio Ciliberto, Ludovic Bellon
We train a neural-network Maxwell's demon to extract work from a model of an underdamped micromechanical cantilever subject to thermal noise. The demon, which periodically adjusts…
Learning to shine: Neuroevolution enables optical control of phase transitions
Sraddha Agrawal, Stephen Whitelam, Pierre Darancet
We address active optical steering of structural phase transitions in solids. We demonstrate that existing evolutionary reinforcement learning approaches can derive optimal time-de…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
Benchmark control problems in nonequilibrium statistical mechanics
Stephen Whitelam, Corneel Casert, Megan Engel +1
We present a set of computer codes designed to test methods for optimizing time-dependent control protocols in fluctuating nonequilibrium systems. Each problem consists of a stocha…
Nonlinear thermodynamic computing out of equilibrium
Stephen Whitelam, Corneel Casert
We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of…
Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth
Leonardo Sabattini, Annalisa Coriolano, Corneel Casert +7
Two-dimensional (2D) materials are poised to revolutionize current solid-state technology with their extraordinary properties. Yet, the primary challenge remains their scalable pro…
Learning efficient erasure protocols for an underdamped memory
Nicolas Barros, Stephen Whitelam, Sergio Ciliberto +1
We apply evolutionary reinforcement learning to a simulation model in order to identify efficient time-dependent erasure protocols for a physical realization of a one-bit memory by…
Learning protocols for the fast and efficient control of active matter
Corneel Casert, Stephen Whitelam
We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles. By encoding the protocol in t…