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. 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 advances in 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.

Agricultural field polygons within smallholder farming systems are essential to facilitate the collection of geo-spatial data useful for farmers, managers, and policymakers. However, the limited availability of training labels poses a challenge in developing supervised methods to accurately delineate field boundaries using Earth observation (EO) data. This letter introduces an open dataset for training and benchmarking machine learning methods to delineate agricultural field boundaries in polygon format. The large-scale dataset consists of 439 001 field polygons divided into 62 tiles of approximately 5× 5 km distributed across Vietnam and Cambodia, covering a range of fields and diverse landscape types. The field polygons have been meticulously digitized from satellite images, following a rigorous multistep quality control process and topological consistency checks. Multitemporal composites of Sentinel-2 (S2) images are provided to ensure cloud-free data. We conducted an experimental analysis testing a state-of-the-art deep learning (DL) workflow based on fully convolutional networks (FCNs), contour closing, and polygonization. We anticipate that this large-scale dataset will enable researchers to further enhance the delineation of agricultural fields in smallholder farms and to support the achievement of the Sustainable Development Goals (SDGs). The dataset can be downloaded from https://doi.org/10.17026/dans-xy6-ngg6.

Vegetated soil plays an essential role in confronting climate change. Soil, together with its ecosystem, stores vast amounts of carbon; it is also the construction material most widely used for the built environment. The expected impacts of climate change, such as extreme wetting-drying cycles, pose an urgent need to understand the interplay between soil deformation, root growth, and water/solute uptake. The key to this challenge lies in the extension of unsaturated soil mechanics to incorporate bio-hydrological processes, such as root growth and water uptake. In this paper, we first provide an overview of the state-of-the-art knowledge of root-zone mechanics and bio-hydrology. We identify the main knowledge gaps and suggest an integrated, bottom-to-top approach to develop a multidisciplinary understanding of soil-water-root interaction. We explain how emerging experimental and numerical methods can be used to study rooted soil under wetting–drying cycles. We focus on the biophysical processes at the scale of plant roots, soil particles and their interfaces, and discuss potential up-scaling to the continuum/field scale. An outlook on possible further research involves effects of temperature and microbial activities.