Out-of-equilibrium simulations with open-to-periodic boundary switching plus a tailored stochastic normalizing flow enable efficient topology sampling in the continuum limit of four-dimensional SU(3) Yang-Mills theory.
Minimal dissipation in processes far from equilibrium
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abstract
A central goal of thermodynamics is to identify optimal processes during which the least amount of energy is dissipated into the environment. Generally, even for simple systems, such as the parametric harmonic oscillator, optimal control strategies are mathematically involved, and contain peculiar and counter-intuitive features. We show that optimal driving protocols determined by means of linear response theory exhibit the same step and $\delta$-peak like structures that were previously found from solving the full optimal control problem. However, our method is significantly less involved, since only a minimum of a quadratic form has to be determined. In addition, our findings suggest that optimal protocols from linear response theory are applicable far outside their actual range of validity.
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In linear response for weakly driven processes, the optimal protocol under constraints on the derivative is linear (constant speed), with minimal work depending only on the integrated relaxation function.
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Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory
Out-of-equilibrium simulations with open-to-periodic boundary switching plus a tailored stochastic normalizing flow enable efficient topology sampling in the continuum limit of four-dimensional SU(3) Yang-Mills theory.
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Linear optimal protocol for physical constraints in weakly driven processes
In linear response for weakly driven processes, the optimal protocol under constraints on the derivative is linear (constant speed), with minimal work depending only on the integrated relaxation function.