Derives Boltzmann distributions for 1D spin models as stochastic processes, quantifies structure via excess entropy and statistical complexity, specifies mechanisms with epsilon-machines, and reports agreement with typical configurations.
Lectures on Phase T ransitions and the Renormalization Group; Frontiers in Physics; Westview Press: Boca Raton, FL, USA, 1992; V olume 85
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Spectral condensation of eigen-microstate occupations, quantified by emergent-sector entropy, diagnoses finite nonequilibrium transitions such as polar-vortex breakdown in ERA5 data and a wave-mean-flow model.
A scale-invariance formulation explains why dimensional analysis succeeds and partitions self-similar solutions into three categories based on whether unit-induced and parameter-induced scale functions coincide.
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What Is a Pattern in Statistical Mechanics? Formalizing Structure and Patterns in One-Dimensional Spin Lattice Models with Computational Mechanics
Derives Boltzmann distributions for 1D spin models as stochastic processes, quantifies structure via excess entropy and statistical complexity, specifies mechanisms with epsilon-machines, and reports agreement with typical configurations.
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Spectral condensation in a finite nonequilibrium atmospheric transition
Spectral condensation of eigen-microstate occupations, quantified by emergent-sector entropy, diagnoses finite nonequilibrium transitions such as polar-vortex breakdown in ERA5 data and a wave-mean-flow model.
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Why dimensional analysis works: general classification of self-similarity based on scale-invariance
A scale-invariance formulation explains why dimensional analysis succeeds and partitions self-similar solutions into three categories based on whether unit-induced and parameter-induced scale functions coincide.