{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/work-probability-distribution-of-weakly","title":"Work probability distribution of weakly driven process in overdamped dynamics","arxiv_id":"2504.05836","date":"2025-04-08","proceeding":null,"authors":["Pierre Nazé"],"abstract":"Analytical work probability distributions for open classical systems are scarce; they can only be calculated in a few examples. In this work, I present a new method to derive such quantities for weakly driven processes in the overdamped regime for any switching time. The white noise Brownian motion in a harmonic linear stiffening trap illustrates the result. The work probability distribution is non-tabulated, with positive, semi-finite support, diverging at the minimal value, and non-Gaussian. An analysis of the range of validity of linear response is made by using the self-consistent criterion of the fluctuation-dissipation relation. The first, second, third, and fourth moments are correctly calculated for small perturbations.","url_abs":"https://arxiv.org/abs/2504.05836v1","url_pdf":"https://arxiv.org/pdf/2504.05836v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"work-probability-distribution-of-weakly","repo_url":"https://github.com/pnaze/PDLR","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}