Graph network-based simulators (GNS) have demonstrated strong potential for learning particle-based physics (such as fluids, deformable solids, and granular flows) while generalizing to unseen geometries due to their inherent inductive biases. However, existing models are typically trained for a single material type and fail to generalize across distinct constitutive behaviors, limiting their applicability in real-world engineering settings. Using granular flows as a running example, we propose a parameter-efficient conditioning mechanism that makes the GNS model adaptive to material parameters. We identify that sensitivity to material properties is concentrated in the early message-passing (MP) layers, a finding we link to the local nature of constitutive models (e.g., Mohr-Coulomb) and their effects on information propagation. We empirically validate this by showing that fine-tuning only the first few (1-5) of 10 MP layers of a pretrained model achieves comparable test performance as compared to fine-tuning the entire network. Building on this insight, we propose a parameter-efficient Feature-wise Linear Modulation (FiLM) conditioning mechanism designed to specifically target these early layers. This approach produces accurate long-term rollouts on unseen, interpolated, or moderately extrapolated values (e.g., up to 2.5 degrees for friction angle and 0.25 kPa for cohesion) when trained exclusively on as few as 12 short simulation trajectories from new materials, representing a 5-fold data reduction compared to a baseline multi-task learning method. Finally, we validate the model's utility by applying it to an inverse problem, successfully identifying unknown cohesion parameters from trajectory data. This approach enables the use of GNS in inverse design and closed-loop control tasks where material properties are treated as design variables.

翻译：基于图网络的模拟器（GNS）因其固有的归纳偏置，在学习基于粒子的物理现象（如流体、可变形固体和颗粒流）并泛化至未见几何结构方面展现出巨大潜力。然而，现有模型通常针对单一材料类型进行训练，难以泛化至不同的本构行为，限制了其在真实工程场景中的应用。以颗粒流为例，我们提出一种参数高效的条件化机制，使GNS模型能够自适应材料参数。我们发现对材料属性的敏感性主要集中在早期消息传递（MP）层，这一现象与本构模型（如莫尔-库仑准则）的局部特性及其对信息传播的影响相关。通过实验验证表明：在预训练模型的10个MP层中仅微调前几层（1-5层）即可达到与微调整个网络相当的测试性能。基于此发现，我们设计了一种参数高效的特征线性调制（FiLM）条件化机制，专门针对这些早期层进行优化。该方法仅需使用新材料的12条短时模拟轨迹进行训练（相比基线多任务学习方法减少5倍数据量），即可在未见、插值或适度外推的参数值（如摩擦角达2.5度、内聚力达0.25 kPa）上实现精确的长期推演。最后，通过将其应用于反问题验证模型实用性，成功从轨迹数据中识别未知的内聚力参数。该方法使得GNS能够应用于反演设计和闭环控制任务，其中材料属性可作为设计变量进行处理。