Controlling the evolution of nonequilibrium systems to minimize dissipated heat or work is a
key goal for designing nanodevices, in both nanotechnology and biology. Progress in …

We use Monte Carlo and genetic algorithms to train neural-network feedback-control
protocols for simulated fluctuating nanosystems. These protocols convert the information …

As demonstrated in the density matrix renormalization group (DMRG) method,
approximating many-body wave function of electrons using a matrix product state (MPS) is a …

Time-dependent protocols that perform irreversible logical operations, such as memory
erasure, cost work and produce heat, placing bounds on the efficiency of computers. Here …

This article presents a new approach to modeling and optimizing individual decision-making
strategies in multi-agent socio-economic systems (MSES). This approach is based on the …

In the limit of small trial moves the Metropolis Monte Carlo algorithm is equivalent to gradient
descent on the energy function in the presence of Gaussian white noise. This observation …

В статье представлен новый подход к моделированию и оптимизации стратегий
принятия индивидуальных решений в многоагентных социально-экономических …

Deep neural networks give us a powerful method to model the training dataset's relationship
between input and output. We can regard that as a complex adaptive system consisting of …

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 …

Statistical physics aims to study how the microscopic properties and interactions of a large
number of entities can be related to their macroscopic behavior. Suppose we have a …