Inductive Thermodynamics from Time Series Data Analysis

We propose an inductive thermodynamics from time series data analysis. Using some modern techniques developed in Statistical Science and Artificial Intelligence, we construct a mathematical model from time series data. We introduce an effective potential for a steady state distribution and construct thermodynamics following the recent work by Sekimoto-Sasa. We apply our idea to neutron noise data from a test nuclear reactor. We interpret the slow transformation of the control bar as work. The response to the transformation appears as excess heat production in accordance with the second law.