{"paper":{"title":"Towards scientific machine learning for granular material simulations -- challenges and opportunities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","physics.comp-ph"],"primary_cat":"cond-mat.soft","authors_text":"Andreas F\\\"urst, Benedikt Alkin, Bram Kieskamp, Daniel Barreto, Daniel N. Wilke, Deepak Tunuguntla, Dingena Schott, Dongwei Ye, Hongyang Cheng, Jin Ooi, Johannes Brandstetter, John Morrissey, Jonathan Nuttall, Krishna Kumar, Luisa Orozco, Marc Fransen, Mengwu Guo, Miguel Angel Cabrera, Stefanos-Aldo Papanicolopulos, Takayuki Shuku, Thomas Weinhart, Tongming Qu, WaiChing Sun, Xinyan Fan","submitted_at":"2025-04-01T09:03:59Z","abstract_excerpt":"Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, a recent Lorentz Center Workshop on \"Machine Learning for Discrete Granular Media\" brought the ML community up to date with GM challenges.\n  This position paper emerged from the workshop discussions. We define granular materials and identify seve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.08766","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.08766/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}