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arXiv:cs.AI· Jongwon Ryu, Sunjae Yoon, Junyeong Kim·· 4 小时前

TIDE/TIDES:面向移动固体交互的交互式 4D 体积液体预测框架

Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction

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研究者提出"交互式 4D 体积液体预测"任务,即根据固体物体的运动方式预测未来稠密 3D 液体速度场的时空演化。为此提出两级循环预测框架 TIDE/TIDES:TIDE 负责局部界面驱动的预测,TIDES 增加传输感知耦合与显式稳定化以减少 rollout 误差累积。在邻接基线、组件消融、运动/尺寸 OOD 测试和长时程评估中,TIDES 取得最佳预测性能,代码已公开。

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Abstract: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 this https URL.
Subjects: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
Cite as: arXiv:2610.16167 [cs.AI]
  (or arXiv:2610.16167v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.16167

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jongwon Ryu [view email]
[v1] Mon, 12 Oct 2026 04:42:05 UTC (3,570 KB)

来源:arXiv:cs.AI · arxiv.org