A machine learning framework learns thermodynamically consistent internal variables and Markovian evolution equations directly from stochastic particle trajectories, validated on an analytically solvable trap model and a phase-transforming chain.
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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics
A machine learning framework learns thermodynamically consistent internal variables and Markovian evolution equations directly from stochastic particle trajectories, validated on an analytically solvable trap model and a phase-transforming chain.