Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 32 references that are in the map. First posted on arXiv 12 Oct 2026.
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References · 27
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
- 2020Learning Mesh-Based Simulation with Graph Networkscited by 868
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- 2021U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flowcited by 297
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- 2025Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domainscited by 62
- 2024APEBench: A Benchmark for Autoregressive Neural Emulators of PDEscited by 60
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- 2025Multiphysics Bench: Benchmarking and Investigating Scientific Machine Learning for Multiphysics PDEscited by 4
- 20253D Neural Operator-Based Flow Surrogates around 3D geometries: Signed Distance Functions and Derivative Constraintscited by 3
- 2025Learning an Implicit Physics Model for Image-based Fluid Simulationcited by 3
- 2026Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interactioncited by 3
- 2025A Mesh-Adaptive Hypergraph Neural Network for Unsteady Flow Around Oscillating and Rotating Structurescited by 1
- 2025NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysiscited by 1
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