7 papers
Contrastive learning in tunable dynamical systems
Menachem Stern, Adam G. Frim, Raúl Candás +2
We generalize the theory of supervised contrastive learning, previously applied to physical systems at equilibrium or steady state, to systems following any dynamics described by c…
Optimal active engines obey the thermodynamic Lorentz force law
Adrianne Zhong, Adam G. Frim, Michael R. DeWeese
What are the fundamental limitations for finite-time engines that extract work from active nonequilibrium systems, and what are the optimal protocols that approach them? We show th…
Shortcut engineering of active matter: run-and-tumble particles
Adam G. Frim, Michael R. DeWeese
Shortcut engineering consists of a class of approaches to rapidly manipulate physical systems by means of specially designed external controls. In this Letter, we apply these appro…
A geometric bound on the efficiency of irreversible thermodynamic cycles
Adam G. Frim, Michael R. DeWeese
Stochastic thermodynamics has revolutionized our understanding of heat engines operating in finite time. Recently, numerous studies have considered the optimal operation of thermod…
Stochastic optimization for learning quantum state feedback control
Ethan N. Evans, Ziyi Wang, Adam G. Frim +2
High fidelity state preparation represents a fundamental challenge in the application of quantum technology. While the majority of optimal control approaches use feedback to improv…
Optimal finite-time Brownian Carnot engine
Adam G. Frim, Michael R. DeWeese
Recent advances in experimental control of colloidal systems have spurred a revolution in the production of mesoscale thermodynamic devices. Functional "textbook" engines, such as…