Fast prediction of suspension rheology is fundamental for optimizing process efficiency and performance in numerous industrial settings. However, traditional simulations are computationally demanding due to explicit evaluation of contact networks and stress tensors in dense regimes approaching shear thickening and jamming. This study presents a microstructure-informed multitask learning framework based on the graph neural network (GNN) that learns an implicit mapping between particle configurations and emergent microstructural and rheological properties of suspensions. This model simultaneously predicts particle pressure $Π$, viscosity $η$, and friction coordination $Z_μ$, in a dynamic steady-state, without explicit knowledge of interparticle forces. Here, semi-dilute to dense suspension systems in 2D were simulated across a wide range of shear stresses $σ$, spanning continuous, discontinuous shear thickening, and shear-jamming conditions. The trained models demonstrated high correlation coefficients ($R^2$ = 0.99) with narrow mean absolute error for packing fractions up to $ϕ\le ϕ_J^μ$ for all predictive targets. However, prediction scatter increases near jamming conditions, attributed to inherent fluctuations in suspension behavior as the critical packing fraction is approached, yet predictions remain in excellent agreement, closely following the trend of the simulated flow curves across stress evolution. Once trained, the model can infer rheological responses directly from structural topology, avoiding explicit stress evaluation during prediction. The approach yields computationally efficient mesoscale surrogates for accelerated simulation with potential for real-time exploration of particulate suspension behavior.
Towards the Multiscale Design of Pressure Sensitive Adhesives
Basque Center for Applied Mathematics（巴斯克应用数学中心）；University of the Basque Country UPV/EHU（巴斯克大学）；Ikerbasque, Basque Foundation for Science（伊克拉巴斯科，巴斯克科学基金会）
Dissipation and microstructure in sheared active suspensions of squirmers
School of Advanced Manufacturing Engineering, Nanjing University（南京大学高级制造工程学院）；Department of Mechanical Engineering and Institute of Applied Mathematics, University of British Columbia（不列颠哥伦比亚大学机械工程系与应用数学研究所）
Beyond binary scission: a generalized three-species cascade breakage model for wormlike micellar solutions
School of Mathematics, Jilin University（吉林大学数学学院）；AI for Science and Engineering Center, Shenzhen Loop Area Institute（深圳_loop_区域研究院科学与工程人工智能中心）；Department of Mathematics, Texas State University（德克萨斯州立大学数学系）
