Tactile perception is key to dexterous manipulation, yet simulating high-resolution elastomer deformation remains computationally prohibitive. Finite element methods (FEM) deliver high fidelity but demand costly remeshing, while Material Point Methods (MPM) suffer from heavy particle-memory tradeoffs. We propose a {reduced-order neural simulation framework} that couples coarse-grained MPM dynamics with an implicit neural decoder to reconstruct sub-particle tactile details from compact latent states. The framework learns a continuous deformation manifold from paired high- and low-resolution simulations, enabling physically consistent, differentiable inference. Compared to the TacIPC, our method achieves over 65\% faster simulation and {40\% lower memory usage}, while maintaining better geometric fidelity. In tactile rendering and 3D surface reconstruction, our methods further improve accuracy by 25\% and produce realistic depth images and surface mesh within a faster inference speed. These results demonstrate that the proposed reduced-order neural model enables high-detail, physically grounded tactile simulation with substantial efficiency gains for robotic interaction and optimization.

翻译：触觉感知是实现灵巧操作的关键，然而高分辨率弹性体变形的模拟在计算上仍面临巨大挑战。有限元方法虽能提供高保真度，但代价高昂的网格重划分过程难以避免；物质点方法则受限于粒子-内存之间的严重权衡。我们提出了一种降阶神经仿真框架，将粗粒度的物质点动力学与隐式神经解码器相结合，从紧凑的隐状态中重构出亚粒子级别的触觉细节。该框架通过联合高、低分辨率模拟数据学习连续变形流形，从而实现物理一致且可微的推理。与TacIPC相比，我们的方法在模拟速度上提升超过65%，内存使用降低40%，同时保持了更高的几何保真度。在触觉渲染与三维表面重建任务中，该方法进一步将精度提升25%，并能以更快的推理速度生成真实的深度图像与表面网格。这些结果表明，所提出的降阶神经模型能够实现高精细、物理可解释的触觉模拟，为机器人交互与优化带来了显著的效率提升。