{"ID":2880793,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14003","arxiv_id":"2508.14003","title":"Machine Learning H-theorem","abstract":"H-theorem provides a microscopic foundation of the Second Law of Thermodynamics and is therefore essential to establishing statistical physics, but at the same time, H-theorem has been subject to controversy that in part persists till this day. To better understand H-theorem and its relation to the arrow of time, we study the equilibration of randomly oriented and positioned hard disks with periodic boundary conditions. Using a model based on the DeepSets architecture, which imposes permutation invariance of the particle labels, we train a model to capture the irreversibility of the H-functional.","short_abstract":"H-theorem provides a microscopic foundation of the Second Law of Thermodynamics and is therefore essential to establishing statistical physics, but at the same time, H-theorem has been subject to controversy that in part persists till this day. To better understand H-theorem and its relation to the arrow of time, we st...","url_abs":"https://arxiv.org/abs/2508.14003","url_pdf":"https://arxiv.org/pdf/2508.14003v3","authors":"[\"Ruben Lier\"]","published":"2025-08-19T17:02:51Z","proceeding":"cond-mat.stat-mech","tasks":"[\"cond-mat.stat-mech\",\"cs.LG\"]","methods":"[]","has_code":false}