Stochastic thermodynamics provides a generic framework for controlling dynamics where fluctuations cannot be neglected. These methods have been deployed to address various optimization problems in passive systems obeying equilibrium constraints as well as in active systems featuring stationary currents. As a field, stochastic thermodynamics has primarily focused on systems with a few degrees of freedom, but recent advances are pushing the framework into more complex settings. This collection, guest edited by Étienne Fodor of the University of Luxembourg and Todd Gingrich of Northwestern University, seeks to highlight novel studies on controlling the dynamics of complex stochastic systems with a rich phenomenology.

The Collection was guest-edited by Étienne Fodor and Todd Gingrich. Every article published in this collection underwent a rigorous peer review process, adhering to the same high standards applied to all papers. The Physical Review E editorial team managed the peer review and made all editorial decisions.

Stochastic resetting is a driving mechanism that is known to minimize the first passage time to reach a target at the cost of energy expenditure. The choice of the physical implementation of each resetting event determines the trade-off between the acceleration of the search process and its energetic cost. The authors find an optimal transport protocol that balances these two quantities.

Modern computing architectures are much more energy dissipative than fundamental thermodynamic limits suggest. The authors implement minimally dissipative logical operations as solutions to optimal transport minimization problems, and develop practical algorithms that achieve near-optimal dissipation and can, in principle, be implemented in realistic experimental setups.

In a one-dimensional gas of Brownian particles subject to simultaneous stochastic resetting, the resetting generates correlations between particles dynamically. These eventually drive the system into a strongly correlated nonequilibrium stationary state, which can be controlled by tuning the inter-reset distribution.

The authors combine a linear-response framework with Lagrangian techniques to minimize the dissipation associated with driving a system between different nonequilibrium steady states. The results show several remarkable properties for the optimal protocol, such as diverging parameters and finite entropy production in the limit of slow driving.

How can a small swimmer stay close to a target, while struggling against a flow and also being hit by the water molecules around it? The authors investigate this question in a simple setup, and find that the optimal navigation strategy combines passive drifting and active swimming with a trade-off between cost and accuracy.

A run-and-tumble particle is placed in a harmonic confining potential, and an external agent can vary the tumbling rate and the strength of the trap over time. The objective is to find time-dependent control protocols steering the system between assigned end states, in a prescribed time interval. Under suitable assumptions, the authors find analytical solutions that are tested against simulations and provide insightful intuition.

In a two-dimensional active lattice gas that undergoes motility-induced phase separation, the authors investigate a morphological transition between a system-spanning slab morphology and a compact droplet. They find that the transition from droplet to slab follows a similar mechanism to its equilibrium counterpart, but the reverse transition depends on rare nonequilibrium fluctuations.

The authors study a model of heat conduction with a bulk driving mechanism resembling the effect of an external field. They find that they can induce the spontaneous formation of traveling energy condensates that exhibit robust long-range temporal order reminiscent of continuous time crystals.

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation