Machine learning-based surrogate models have emerged as more efficient alternatives to numerical solvers for physical simulations over complex geometries, such as car bodies. Many existing models incorporate the simulation mesh as an additional input, thereby reducing prediction errors. However, generating a simulation mesh for new geometries is computationally costly. In contrast, mesh-free methods, which do not rely on the simulation mesh, typically incur higher errors. Motivated by these considerations, we introduce SMART, a neural surrogate model that predicts physical quantities at arbitrary query locations using only a point-cloud representation of the geometry, without requiring access to the simulation mesh. The geometry and simulation parameters are encoded into a shared latent space that captures both structural and parametric characteristics of the physical field. A physics decoder then attends to the encoder's intermediate latent representations to map spatial queries to physical quantities. Through this cross-layer interaction, the model jointly updates latent geometric features and the evolving physical field. Extensive experiments show that SMART is competitive with and often outperforms existing methods that rely on the simulation mesh as input, demonstrating its capabilities for industry-level simulations.

翻译：基于机器学习的替代模型已作为数值求解器更高效的替代方案出现，用于复杂几何体（如汽车车身）的物理仿真。许多现有模型将仿真网格作为附加输入，从而降低了预测误差。然而，为新几何体生成仿真网格的计算成本高昂。相比之下，不依赖仿真网格的无网格方法通常会产生更高的误差。基于这些考量，我们引入了SMART——一种神经替代模型，它仅使用几何体的点云表示，无需访问仿真网格，即可在任意查询位置预测物理量。几何体与仿真参数被编码到一个共享的潜空间中，该空间捕获物理场的结构与参数特征。随后，一个物理解码器通过关注编码器的中间潜表征，将空间查询映射为物理量。通过这种跨层交互，模型联合更新潜几何特征与演化的物理场。大量实验表明，SMART在与依赖仿真网格输入的现有方法竞争时表现相当且常胜出，展示了其面向工业级仿真的能力。