A quadratic classifier logit built from learned state embeddings converts forward-versus-reversed trajectory discrimination into a scalable entropy-production estimate plus a low-dimensional map of irreversible flow.
Directly learning (P,⃗b) eliminates the need for a large network and sig- nificantly reduces parameter count
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cond-mat.stat-mech 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Identifying nonequilibrium degrees of freedom in high-dimensional stochastic systems
A quadratic classifier logit built from learned state embeddings converts forward-versus-reversed trajectory discrimination into a scalable entropy-production estimate plus a low-dimensional map of irreversible flow.