Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finite material points that are tracked through the evolving velocity field. Due to the particle-like nature of the simulation, graph neural networks (GNNs) have emerged as appealing and successful surrogates. However, the practical utility of such GNN-based simulators relies on their ability to faithfully model physics, providing accurate and stable predictions over long time horizons - which is a notoriously hard problem. In this work, we identify particle clustering originating from tensile instabilities as one of the primary pitfalls. Based on these insights, we enhance both training and rollout inference of state-of-the-art GNN-based simulators with varying components from standard SPH solvers, including pressure, viscous, and external force components. All neural SPH-enhanced simulators achieve better performance, often by orders of magnitude, than the baseline GNNs, allowing for significantly longer rollouts and significantly better physics modeling. Code available under (https://github.com/tumaer/neuralsph).

翻译：平滑粒子流体动力学（SPH）在现代工程与科学学科中无所不在。SPH是一类拉格朗日格式，通过追踪演化速度场中的有限物质点来离散化流体动力学。由于该模拟的类粒子特性，图神经网络（GNN）已作为有前景且成功的替代模型出现。然而，此类基于GNN的模拟器的实际效用依赖于其忠实建模物理的能力，即在长时间跨度内提供准确且稳定的预测——这本身是一大公认难题。在本工作中，我们将源于拉伸不稳定的粒子聚类识别为主要缺陷之一。基于这些见解，我们利用标准SPH求解器中的不同组件（包括压力项、粘性项和外力项）增强了最先进的基于GNN的模拟器的训练与推理过程。所有经神经SPH增强的模拟器在性能上均优于基线GNN（通常提升数个数量级），支持显著更长的推理轨迹并实现显著更优的物理建模。代码详见(https://github.com/tumaer/neuralsph)。