A Kinetic View of Statistical Physics

By Paul L. Krapivsky, Sidney Redner, E. Ben-Naim. 2010

980 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a3d1df85333508a8d7534a96eeb620f39457c834

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Citations in the corpus

Moment Closure—A Brief Review (2015, unknown): “A different context where moment methods are used frequently is kinetic theory [22,83, 93].”
The problem of pattern and scale in ecology: what have we learned in 20 years? (2013, background): “From Krapivsky et al. (2010).”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, background): “A variety of models were introduced to gain insights into binary-state dynamics on complex networks [42], such as the votermodels for competition of twoopinions [437], stochastic propagationmodels for epidemic spreading [438],models of rumor diffusion and adoption of new technologies [439], cascadin”
Anomalous critical and supercritical phenomena in explosive percolation (2015, background): “ndom graphs. See Ref. [14] for details of the convergence and concentration assumptions underlying this approach. Cluster aggregation approaches to general percolation provide useful analytical tools [6, 61], which have been quite conducive for modeling EP processes [62, 24, 33]. In their arguments ”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “A stochastic dynamics based on symmetric couplings entails detailed balance, leading to a steady state described by the Boltzmann distribution [118], whereas asymmetric couplings lead to a non-equilibrium steady state.”
Nonequilibrium Markov Processes Conditioned on Large Deviations (2014, background): “For general introductions to Markov processes and their applications in physics, see [52–54,80–82]; for references on the mathematics of these processes, see [3,38,39,83,84].”
Nonequilibrium Thermodynamics of Chemical Reaction Networks: Wisdom from Stochastic Thermodynamics (2016, background): “[130] Paul Krapivsky, Sidney Redner, and Eli Ben-Naim, A Kinetic View of Statistical Physics (Cambridge University Press, 2010).”
From quantum chaos and eigenstate thermalization to statistical mechanics and thermodynamics (2015, background): “By doing that, one recovers the detailed balance condition in its most familiar form [211]: pI(nI → mI) pI(mI → nI) ≈ e −βII(EII)δEI mn , (166)”
Diffusion with resetting in arbitrary spatial dimension (2014, background): “The nature and properties of nonequilibrium stationary state are questions of fundamental importance within statistical physics [2].”
Stochastic population dynamics in spatially extended predator–prey systems (2017, methodology): “The individual-based models (18)-(23) are defined by the corresponding Markov processes and the dynamics is governed by the underlying master equation [154, 57, 155, 60].”
“Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology (2019, methodology): “Applications 3.1. Algorithms for aggregation and fragmentation equations In our benchmarks we used parallel implementation of ecient numerical methods for the aggregation and fragmentation equations [26, 22] and also parallel implementation of the solver for advection-driven coagulation process [24]”
Network propagation: a universal amplifier of genetic associations (2017, background): “…; the data science community applies variants of the Google PageRank search algorithm 12,16 ; statistical physicists study heat diffusion processes 17,18 ; electrical engineers compute minimum energy states within an electrical circuit 14,19 ; and the machine-learning community considers…”
Binary-state dynamics on complex networks: pair approximation and beyond (2012, unknown): “Here, each node is in either the spin-up or spin-down state, and transitions occur according to dynamical rules that minimize the Hamiltonian of ferromagnetic interactions [3,66].”
Motility-Induced Phase Separation (2014, background): “square gradient term with constant coecient to the free-energy functional of the equivalent passive system: F= Z [f(ˆ) + 2 (rˆ)2]ddr (41) where f(ˆ) obeys Eq. 27. Hence = F=ˆ= lnˆ+ lnv(ˆ) r2ˆ (42) This maintains the mapping between MIPS and passive phase separation (37), and leads to the well known”
Random walks and diffusion on networks (2016, background): “375, 376]. 41 5.7. Consensus probability and time of voter models Voter models are a prototypical family of models of opinion formation that are often dened in terms of a Markov process on a network [1, 31, 33, 49, 377{379]. In traditional voter models, each node assumes one of two opinions, which w”