New tool maps where energy gets wasted in complex systems
Researchers have developed a method to pinpoint exactly where and when energy dissipates in far-from-equilibrium processes—from chemical reactions to biological systems. The technique combines machine learning with thermodynamic principles to extract this information directly from experimental data, potentially helping industries optimize processes and reduce losses.
Originaltitel: Localising entropy production along non-equilibrium trajectories
Abstract Entropy production is a universal measure of irreversibility and energy dissipation in physical, chemical, and biological systems operating far from equilibrium. However, quantifying and spatiotemporally localising it in complex processes directly from experimental data remains a major open challenge. Here we address this issue through a data-driven approach that combines the recently developed short-time thermodynamic uncertainty relation based inference scheme with machine learning techniques. Our approach uses the flexible function representation provided by deep neural networks to achieve accurate reconstruction of high-dimensional, potentially time-dependent dissipative force fields as well as the localization of fluctuating entropy production in both space and time along nonequilibrium trajectories. We demonstrate the versatility of the framework through applications to diverse systems of fundamental interest and experimental significance, where it successfully addresses distinct challenges in localising entropy production.