Particle-based liquid simulation is widely used in graphics and physical modeling, but high-resolution rollouts remain computationally expensive. Consequently, many methods aim to recover fine-scale dynamics and dense transport patterns from coarse particle simulations. However, these methods typically rely on additional particle generation, which still incurs considerable computational overhead and leads to poor representation. To this end, we propose AnisoLift, a structured latent closure framework that augments each coarse particle with learnable anisotropic ellipsoidal components. This allows the model to capture directional local structure from the underlying high-resolution flow without introducing extra particles. Given a coarse simulation, our model predicts residual corrections to particle states to bring the updated state closer to the aligned high-resolution teacher. Our training objective jointly supervises particle dynamics and anisotropic geometric structure, encouraging both physical consistency and structural coherence. Extensive experiments show that our approach enhances coarse liquid simulations through improving fidelity to fully resolved flow behavior.

翻译：基于粒子的液体模拟广泛用于图形学和物理建模，但高分辨率模拟的计算成本仍然很高。因此，许多方法旨在从粗颗粒模拟中恢复精细尺度的动力学和密集传输模式。然而，这些方法通常依赖于额外的粒子生成，这不仅导致显著的计算开销，还造成了表征能力不足。为此，我们提出AnisoLift，一种结构化的潜在闭合框架，通过为每个粗颗粒附加可学习的各向异性椭球分量，使模型能够从底层高分辨率流中捕获方向性局部结构，而无需引入额外粒子。给定粗颗粒模拟，我们的模型预测粒子状态的残差修正，以使更新后的状态更接近对齐的高分辨率教师模型。我们的训练目标同时监督粒子动力学和各向异性几何结构，从而促进物理一致性和结构连贯性。大量实验表明，我们的方法通过提高对完全解析流行为的保真度，增强了粗颗粒液体模拟的效果。