Abstract
Controlling thermodynamic cycles to minimize the dissipated heat is a longstanding goal in thermodynamics, and more recently, a central challenge in stochastic thermodynamics for nanoscale systems. Here, we introduce a theoretical and computational framework for optimizing nonequilibrium control protocols that can transform a system between two distributions in a minimally dissipative fashion. These protocols optimally transport a system along a Wasserstein geodesic, paths through the space of probability distributions that minimize the dissipative cost of a transformation. Furthermore, we show that the thermodynamic metric – determined via a linear response approach – can be directly derived from a more general formulation based on optimal transport distances, thus providing a unified perspective on thermodynamic geometries. We investigate this unified geometric framework in two model systems and observe that our procedure for optimizing control protocols is robust beyond linear response.
Graduate Student
Shriram is a PhD student in the Department of Chemistry at Stanford University, where he is advised by Grant Rotskoff. He is broadly interested in utilizing both data driven and theoretical approaches to more robustly simulate and characterize biological systems. His current work involves using Reinforcement Learning methods to control systems driven away from equilibrium. Shriram received a B.S. in Biological Chemistry with Honors from The University of Chicago, where he also completed a minor in Computer Science. At UChicago, Shriram worked on quantifying the kinetics of small RNA molecule regulation under the mentorship of Jingyi Fei. Outside the lab, Shriram is an avid Chicago sports fan, enjoys watching movies and exploring the local restaurant scene.