Fast driving across first-order transitions in relativistic scalar fields produces temperature- and dimension-independent finite-time scaling matching mean-field theory, crossing over to Kibble-Zurek scaling near criticality and nucleation-dominated dynamics at low temperatures.
Onsager ,\ title title Crystal Statistics
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
PDNS decomposes diffusion neural sampler training into proximal subproblems on path measures to gradually approach multimodal targets and promote mode exploration.
DFT plus Monte Carlo simulations predict itinerant ferromagnetism with Tc exceeding 160 K in p-doped MoS2 monolayers at 9% V concentration.
Lecture notes that treat statistical physics as probability theory and connect Ising models, spin glasses, and renormalization group ideas to Hopfield networks, restricted Boltzmann machines, and large language models.
citing papers explorer
-
Non-equilibrium scaling across first-order transitions with relativistic scalar fields
Fast driving across first-order transitions in relativistic scalar fields produces temperature- and dimension-independent finite-time scaling matching mean-field theory, crossing over to Kibble-Zurek scaling near criticality and nucleation-dominated dynamics at low temperatures.
-
Proximal Diffusion Neural Sampler
PDNS decomposes diffusion neural sampler training into proximal subproblems on path measures to gradually approach multimodal targets and promote mode exploration.
-
Itinerant Ferromagnetism in p-doped Monolayers of MoS2
DFT plus Monte Carlo simulations predict itinerant ferromagnetism with Tc exceeding 160 K in p-doped MoS2 monolayers at 9% V concentration.
-
Lecture Notes on Statistical Physics and Neural Networks
Lecture notes that treat statistical physics as probability theory and connect Ising models, spin glasses, and renormalization group ideas to Hopfield networks, restricted Boltzmann machines, and large language models.