[2603.26800] DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting

Computer Science > Machine Learning arXiv:2603.26800 (cs) [Submitted on 26 Mar 2026] Title:DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting Authors:Huanshuo Dong, Hao Wu, Hong Wang, Qin-Yi Zhang, Zhezheng Hao View a PDF of the paper titled DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting, by Huanshuo Dong and 4 other authors View PDF HTML (experimental) Abstract:Long-term fluid dynamics forecasting is a critically important problem in science and engineering. While neural operators have emerged as a promising paradigm for modeling systems governed by partial differential equations (PDEs), they often struggle with long-term stability and precision. We identify two fundamental failure modes in existing architectures: (1) local detail blurring, where fine-scale structures such as vortex cores and sharp gradients are progressively smoothed, and (2) global trend deviation, where the overall motion trajectory drifts from the ground truth during extended rollouts. We argue that these failures arise because existing neural operators treat local and global information processing uniformly, despite their inherently different evolution characteristics in physical systems. To bridge this gap, we propose the Dual-Scale Neural Operator (DSO), which explicitly decouples information processing into two complementary modules: depthwise separable convolutions for fine-grained local feature extraction and an MLP-Mixer for long-r...