[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122383-en":3,"doc-seo-122383-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122383,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Towards Scientific Machine Learning for Granular Material Simulations - Challenges and Opportunities","Micro-scale mechanisms, including inter-particle and particle-fluid interactions, determine granular systems’ behaviour. Particle-scale simulations deliver detailed insight but often remain computationally prohibitive. A Lorentz Center workshop on Machine Learning for Discrete Granular Media highlighted how ML can support constitutive law development, efficient data-driven surrogates, and uncertainty quantification. This position paper defines granular materials, identifies seven cross-scale challenges, and proposes a workflow and representative examples for ML surrogate development in solid-like and fluid-like regimes.","Archives of Computational Methods in Engineering [https://doi.org/10.1007/s1](https://doi.org/10.1007/s1) 1831-025-10322-8  \nTowards Scientific Machine Learning for Granular Material Simulations: Challenges and Opportunities  \nMarc Fransen1 · Andreas Fürst2 · Deepak Tunuguntla3 · Daniel N. Wilke4,15 · Benedikt Alkin2,16 · Daniel Barreto5 · Johannes Brandstetter2,16 · Miguel Angel Cabrera6 · Xinyan Fan6 · Mengwu Guo7 · Bram Kieskamp8 ·  \nKrishna Kumar9 · John Morrissey10 · Jonathan Nuttall1 · Jin Ooi10 · Luisa Orozco11 · Stefanos‑Aldo Papanicolopulos10 · Tongming Qu12 · Dingena Schott6 · Takayuki Shuku13 · WaiChing Sun14 · Thomas Weinhart8 · Dongwei Ye8 · Hongyang Cheng8  \nReceived: 2 April 2025 / Accepted: 23 July 2025 © The Author(s) 2025  \nAbstract  \nMicro-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. At a recent Lorentz Center Workshop on “Machine Learning for Discrete Granular Media”, researchers explored how machine learning approaches can aid the development of constitutive laws and efficient data-driven surrogates for granular materials while also addressing uncertainty quantification. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, the workshop brought the ML community up to date with GM challenges. This position paper emerged from the workshop discussions. In this position paper, we define granular materials and identify seven key challenges that characterise their distinctive behaviour across various scales and regimes–ranging from gas-like to fluid-like and solid-like. Addressing these challenges is essential for developing robust and efficient models for the digital twinning of granular systems in various industrial applications. To showcase the potential of ML to the GM community, we present classical and emerging machine/deep learning techniques that have been, or could be, applied to granular materials. We reviewed sequence-based learning models for path-dependent constitutive behaviour, followed by encoder-decoder type models for representing high-dimensional data in reduced spaces. We then explore graph neural networks and recent advancesin neural operator learning. The latter captures the emerging field evolution of interacting particles via efficient latent space representation. Lastly, we discuss model-order reduction and probabilistic learning techniques for high-dimensional parameterised systems, both of which are crucial for quantifying and incorporating uncertainties arising from physics-based and data-driven models. We present a typical workflow aimed at unifying data structures and modelling pipelines and guiding readers through the selection, training, and deployment of ML surrogates for granular material simulations. Finally, we illustrate the workflow’s practical use with two representative examples, focusing on granular materials in solid-like and fluid-like regimes.  \n1 Introduction  \nGranular materials (GMs), ranging from beach sand to raw materials such as iron ore, are integral to various industrial processes. They play an essential role across many engineering disciplines, including geotechnical [1], coastal [2],  \nMarc Fransen, Andreas Fürst, Deepak Tunuguntla and Daniel N. Wilke have equal contributions as co-first authors. Hongyang Cheng is the corresponding author. All other co-authors are listed alphabetically.  \nExtended author information available on the last page of the article  \nhydraulic engineering [3], pharmaceutical [3], additive manufacturing [4–6], agriculture [7, 8], bulk handling [9–11] and robotics [12, 13] .  \nAlthough simple in appearance, as illustrated in Fig. 1, GMs exhibit solid-, fluid-, and gas-like behaviour [14], making it one of the most complex materials to handle. A firstprinciples approach for simulating GM beh","cbCaihveY4RjK6s5","https://ap.wps.com/l/cbCaihveY4RjK6s5","pdf",3851819,1,31,"English","en",105,"# Abstract\n# 1 Introduction\n## Granular materials in engineering applications\n## Challenges of grain-scale vs continuum modeling\n## Need for unified multi-scale continuum theories","[{\"question\":\"Why are granular material simulations computationally expensive at the particle scale?\",\"answer\":\"Particle-scale simulations resolve inter-particle and particle-fluid interactions, but the required granularity makes computation prohibitive for practical use.\"},{\"question\":\"What seven challenges are addressed for granular materials across regimes?\",\"answer\":\"The paper identifies seven key challenges that characterise granular behaviour across multiple scales and regimes, covering transitions from gas-like to fluid-like and solid-like responses.\"},{\"question\":\"How does scientific machine learning support granular simulations and modeling pipelines?\",\"answer\":\"It provides sequence-based and encoder-decoder models, graph neural networks, neural operator approaches, and probabilistic or model-order reduction methods, alongside a unified workflow to select, train, and deploy ML surrogates with uncertainty handling.\"}]","Towards Scientific Machine Learning for Granular Material Simulations - 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