TIDE/TIDES：预测运动固体扰动下4D液体速度场演化
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction
研究人员把液体预测做成了条件预测任务：输入固体怎么动，输出整个3D液体速度场怎么变，代码开源，做物理仿真或机器人操作的可以看看。
论文提出新任务 Interactive 4D Volumetric Liquid Forecasting：给定固体在液体中的运动方式，预测未来稠密 3D 液体速度场的时空演化。作者设计了 TIDE/TIDES 两级循环预测框架，TIDE 负责局部的液固界面驱动预测，TIDES 加入传输感知耦合与显式稳定化来降低长时程 rollout 误差。在相邻基线对比、组件消融、运动与尺寸 OOD 测试和长时程评估中，TIDES 取得最佳预测结果。该任务可服务于机器人液体操作、数字孪生和刚体-流体视频生成等下游场景，代码已在 GitHub 开源。
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction
We propose the task of Interactive 4D Volumetric Liquid Forecasting: predicting the spatiotemporal evolution of a future dense 3D liquid velocity field conditioned on how a solid object moves through it. This task provides an upstream physical forecasting primitive for systems that need to reason about motion-conditioned liquid response, including robotic liquid manipulation, digital twins, and rigid-fluid video generation. Existing works have made efforts in predicting flow around static or prescribed solid geometries and synthesizing visually plausible fluid motion. Yet these approaches provide limited coverage for interactive liquid dynamics, as they are not capable of forecasting how a moving solid induces a future full-domain liquid response. In this forecasting scenario, the hydrodynamic effect is condensed near an evolving liquid-solid interface, but the prediction target is a global spatiotemporal rollout. To address this challenge, we propose a two-level recurrent forecasting framework, TIDE/TIDES: TIDE isolates localized interface-driven forecasting, while TIDES adds transport-aware coupling and explicit stabilization to reduce accumulated rollout error. Across adapted neighboring baselines, controlled component ablations, motion/size OOD tests, and long-horizon evaluation, TIDES achieves the best forecasting performance and the ablations identify localized interaction, transport coupling, and stabilization as the key contributors. Together, our task formulation, benchmark, and TIDE/TIDES study establish a controlled foundation for moving-solid-conditioned dense liquid forecasting. The code is publicly available at https://github.com/jongwonryu/TIDE-TIDES.
