Thermodynamic Observables

Thermodynamic observables research focuses on efficiently and accurately measuring and predicting system behavior, particularly in complex systems where direct measurement is difficult or impossible. Current research emphasizes developing data-driven methods, employing machine learning techniques like normalizing flows, neural networks (including convolutional and Fourier Neural Operators), and Koopman operators, to learn relationships between system parameters and observable quantities, often addressing challenges like mode collapse and high dimensionality. These advancements have significant implications for diverse fields, including high-energy physics (improving simulations and data analysis), autonomous systems (assessing autonomy levels), and control theory (designing effective control strategies for partially observable systems).