Neural-PD: A Peridynamics-Inspired Neural Simulator for 3D Fracture Dynamics

Predicting fracture in deformable objects remains challenging for neural dynamics models because cracks introduce localized, irreversible discontinuities. Peridynamics (PD) is a well-established continuum physical simulation framework whose interaction operators share the same structure as deep networks on point clouds. Inspired by Peridynamics, we present Neural-PD, a particle-based neural simulator that uses the interaction structure of PD as a physical inductive bias for learning deformation, contact, and fracture. Different from other particle-based neural dynamics models, Neural-PD follows Peridynamics in combining material forces and contact forces, while utilizing each type of force on a different neighborhood: material forces over the reference configuration and contact forces over the current one. An attention-based formulation inspired by PD explicitly models the strength of each bond, which helps predicting fracture and damage. Unlike traditional peridynamics, Neural-PD adopts a U-Net architecture that propagates force information across spatial scales, supporting long-range dynamics predic- tion at large time steps. Due to the lack of benchmark for evaluating neural dy- namics models in damage and fracture scenarios, we introduce a new benchmark spanning visco-elastic deformations and multiple crack and damage scenarios. On these benchmarks, Neural-PD achieves 38% − 93% lower error than other neural dynamics models, showcasing the significant potential of incorporating particle-based physics ideas such as PD in neural dynamics modeling.