arXiv AI
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction
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arXiv Machine Learning
Oct 3
Variational Streaming Flow: Probabilistic Forecasting in Physical Time
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.
arXiv Machine Learning
Jun 10
COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting
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.
arXiv Computer Vision
6d ago
UFMF: Radar-Guided Kinematic Priors for Real-Time 4D Occupancy Forecasting
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.
arXiv Machine Learning
Aug 6
TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning
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.
Hugging Face Trending Papers
Jul 8
BubbleSH: A Dataset of Rising Bubbles with Deformable Interfaces
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.
arXiv AI
Oct 31
WorldFlow: Joint Video and 3D Motion Prediction via Large-Scale Pretraining
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.