Large-time asymptotics of the gyration radius for long-range statistical-mechanical models
Akira Sakai

TL;DR
This paper discusses the derivation of precise asymptotic behaviors of the gyration radius in various long-range statistical-mechanical models, such as random walk, self-avoiding walk, and oriented percolation, above their critical dimensions.
Contribution
It provides a simplified explanation of how to obtain sharp asymptotics for the gyration radius in these models above their upper critical dimension.
Findings
Sharp asymptotics of the gyration radius derived
Applicable to models like random walk and percolation
Above the model-dependent upper critical dimension
Abstract
The aim of this short article is to convey the basic idea of the original paper [3], without going into too much detail, about how to derive sharp asymptotics of the gyration radius for random walk, self-avoiding walk and oriented percolation above the model-dependent upper critical dimension.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComputational Geometry and Mesh Generation · Scientific Research and Discoveries · Point processes and geometric inequalities
