Variational Streaming Flow (VSF) extends the efficient Streaming Flow (SF) framework by learning a latent distribution conditioned on system dynamics, enabling probabilistic forecasting in physical time. Unlike SF’s deterministic velocity field, VSF produces multiple plausible future trajectories, improving predictive accuracy and distributional fidelity across deterministic and stochastic dynamical systems. The method supports long‑horizon rollouts over 1,000 steps, handles bifurcating dynamics, and can be integrated as a plug‑and‑play predictor into Joint‑Embedding Predictive Architecture (JEPA) world models to enhance navigation, motion planning, and manipulation tasks.
By Hans Hao-Hsun Hsu, Minseon Gwak, Soon Hoe Lim, Pan Li, N. Benjamin Erichson
arXiv:2606. 11162v1 Announce Type: new Abstract: In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes.
By Zesheng Liu, Maryam Rahnemoonfar
UFMF (Ultra-Fast Multi-modal Forecaster) is a real‑time 4D occupancy forecasting model that uses a lightweight diagonal state‑space core and a radar‑guided velocity‑aware dynamic prior to predict future motion. The prior splits future displacement into a linear extrapolation and a learned residual, enabling per‑cell flow fields that carry a compact dynamic latent subspace during rollout. With auxiliary world‑frame dynamic supervision and class‑conditioned vertical‑profile supervision, UFMF achieves 122 FPS on an RTX 4090 and outperforms recent occupancy world models on nuScenes while using far less computational resources.
By Zeping Zhang, Zhuoya Zhao, Robert Lagani\`ere
arXiv:2608. 04222v1 Announce Type: cross Abstract: Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly.
By Yilong Dai, Yiming Sun, Yiheng Chen, Shengyu Chen, Peyman Givi, Xiaowei Jia, Runlong Yu
Bubbly flows exhibit complex multiscale dynamics, with deformable bubbles interacting through the surrounding liquid and giving rise to strongly coupled kinematic and morphological behavior. We present BubbleSH, a bubbly flows dataset consisting of transient, three-dimensional bubble-swarm dynamics obtained from high-fidelity direct numerical simulations of bubbles rising in a periodic domain.
WorldFlow is a generative world model that jointly predicts future video frames and query-based 3D trajectories using a unified Mixture-of-Transformers backbone. It represents 3D motion as continuous displacement tokens aligned with the video grid and optimizes a rectified-flow objective to couple explicit geometric dynamics with visual evolution. Trained on 1.44 million heterogeneous samples and evaluated on WorldFlowBench, it achieves the lowest average and final displacement errors among learning-based methods, notably outperforming baselines on spatial reasoning tasks.
By Yuhao Lu, Jiongxu Chen, Hexiang Wei, Jiajin Wu, Juelin Li, Mingjian Liang, Fan Yang, Zhuowei Liang, Zihao Liu, Zhennan Wang, Sheng Wang, Lin Shao
arXiv:2606. 13817v1 Announce Type: cross Abstract: World models in robot learning predict future states from visual observations and actions, enabling agents to reason about the consequences of their controls.
By Yitao Jiang, Luyang Zhao, Muhao Chen, Devin Balkcom
arXiv:2610.03759v3 Announce Type: replace-cross
Abstract: Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systemat...
By Siyu Gan, Dongsheng Luo, Kunxiaojia Yuan, Dongjin Song, Jingchao Ni
The paper introduces flux‑form spatiotemporal neural operators for predicting coarse‑grained dynamics of multiscale PDEs without relying on closure models. It learns a surrogate evolution operator from filtered high‑fidelity data, using Fourier convolution for spatial mixing and a causal kernel with time‑lag attention for temporal mixing. The method incorporates a flux‑form inductive bias to maintain conservation and provides a data‑driven rule for selecting memory length, achieving stable, accurate long‑horizon rollouts on benchmark equations and turbulent flow simulations.
By Junfeng Chen
arXiv:2606.18558v2 Announce Type: replace
Abstract: Motion forecasting is central to visual intelligence: agents must anticipate how objects will move in order to plan actions, reason about physical...
By Jianing Zhang, Chenhao Zheng, Yajun Yang, Max Argus, Rustin Soraki, Winson Han, Taira Anderson, Chun-Liang Li, Shuo Liu, Jiafei Duan, Zhongzheng Ren, Jieyu Zhang, Ranjay Krishna
arXiv:2609.36504v1 Announce Type: new
Abstract: Accurate prediction of three-dimensional (3D) microstructure evolution remains computationally demanding because high-fidelity phase-field simulations...
By Michael Trimboli, Wenxi Liu, Xianqi Li
The paper introduces a physics‑refined framework for spatiotemporal forecasting on open‑boundary hydrologic graphs, addressing instability caused by missing external boundary forcing. It learns ghost node proxies to approximate unobserved inputs and applies two physics refiners: one enforcing local consistency with two‑hop neighbors, and another using a physics‑guided graph neural operator to reduce long‑horizon drift. Experiments on two real‑world hydrologic graphs show improved prediction accuracy and stability compared to existing learning‑based and physics‑informed models.
By Haoyang Jiang, Zhengui Wang, Shenghan Gao, Y. Joseph Zhang, Xingquan Zhu, Yi He