Learning a physical system's energy from data is shown to work better when the data include dissipation, because dissipation breaks the hidden-symmetry (Casimir) degeneracy that pure reversible dynamics cannot reveal.
Time-Reversal and Entropy
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abstract
There is a relation between the irreversibility of thermodynamic processes as expressed by the breaking of time-reversal symmetry, and the entropy production in such processes. We explain on an elementary mathematical level the relations between entropy production, phase-space contraction and time-reversal starting from a deterministic dynamics. Both closed and open systems, in the transient and in the steady regime, are considered. The main result identifies under general conditions the statistical mechanical entropy production as the source term of time-reversal breaking in the path space measure for the evolution of reduced variables. This provides a general algorithm for computing the entropy production and to understand in a unified way a number of useful (in)equalities. We also discuss the Markov approximation. Important are a number of old theoretical ideas for connecting the microscopic dynamics with thermodynamic behavior.
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2019 1verdicts
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Learning Physics from Data: a Thermodynamic Interpretation
Learning a physical system's energy from data is shown to work better when the data include dissipation, because dissipation breaks the hidden-symmetry (Casimir) degeneracy that pure reversible dynamics cannot reveal.