Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators

ArXi:2511.05456v2 Announce Type: replace Graph network-based simulators (GNS) have nstrated 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.