Derivation of the stochastic Burgers equation from totally asymmetric interacting particle systems
From MaRDI portal
Publication:2105069
DOI10.1016/j.spa.2022.10.006OpenAlexW4306405345MaRDI QIDQ2105069
Publication date: 8 December 2022
Published in: Stochastic Processes and their Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spa.2022.10.006
Interacting random processes; statistical mechanics type models; percolation theory (60K35) Stochastic partial differential equations (aspects of stochastic analysis) (60H15)
Related Items (1)
Cites Work
- Solving the KPZ equation
- From duality to determinants for \(q\)-TASEP and ASEP
- A theory of regularity structures
- A stochastic Burgers equation from a class of microscopic interactions
- KPZ reloaded
- Tightness of probabilities on C([0,1;\(S_ p\)) and D([0,1];\(S_ p\))]
- Superdiffusivity of the 1D lattice Kardar-Parisi-Zhang equation
- Central limit theorem for additive functionals of reversible Markov processes and applications to simple exclusions
- Stochastic Burgers and KPZ equations from particle systems
- Probabilistic approach to the stochastic Burgers equation
- Scaling of the sasamoto-Spohn model in equilibrium
- Brownian analogues of Burke's theorem.
- Diffusion of directed polymers in a random environment.
- Stationary directed polymers and energy solutions of the Burgers equation
- The infinitesimal generator of the stochastic Burgers equation
- The Kardar-Parisi-Zhang equation as scaling limit of weakly asymmetric interacting Brownian motions
- Macdonald processes
- Nonlinear fluctuations of weakly asymmetric interacting particle systems
- THE KARDAR–PARISI–ZHANG EQUATION AND UNIVERSALITY CLASS
- PARACONTROLLED DISTRIBUTIONS AND SINGULAR PDES
- Energy solutions of KPZ are unique
- Dynamic Scaling of Growing Interfaces
- Some recent progress in singular stochastic partial differential equations
This page was built for publication: Derivation of the stochastic Burgers equation from totally asymmetric interacting particle systems