Applications of the Monte Carlo Method in Statistical Physics

By Kurt Binder. 2012

671 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/2fb8849de76bc3b9b50210217ce085c322332af0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Hans-Josef Schulze
2. Sharon C. Glotzer
3. J. Wei
4. Wolfgang Paul
5. Isaac Vidaña
6. G. Fiorella Burgio

In Mechanical Engineering

1. Hans-Josef Schulze
2. Sharon C. Glotzer
3. J. Wei
4. Wolfgang Paul
5. Isaac Vidaña
6. G. Fiorella Burgio

Citations in the corpus

• Fermion Monte Carlo without fixed nodes: a game of life, death, and annihilation in Slater determinant space. (2009, neutral): “It has long been known that the only major obstacle preventing the exact numerical simulation of manyelectron systems via stochastic methods such as Diffusion quantum Monte Carlo (DMC) [1] or the related Green’s function Monte Carlo (GFMC) [2] is the Fermion sign problem [3].”
• Molecular and Mesoscale Simulation Methods for Polymer Materials (2002, methodology): “It is known, for example, that suitably chosen local update rules reproduce the Rouse-like dynamics of short polymer chains (105, 107).”
• Neutron stars and the nuclear equation of state (2021, methodology): “However, the accuracy of the different QMC versions is limited by the so-called fermion sign problem [133], for which different approximations are adopted [134].”