FR-Mamba: Time-Series Physical Field Reconstruction Based on State Space Model
This addresses the challenge of suboptimal performance in time-evolving physical systems for domains like fluid dynamics and thermodynamics, representing a novel method for a known bottleneck.
The paper tackles the problem of physical field reconstruction from limited sensor measurements by proposing FR-Mamba, a framework that combines Fourier Neural Operator and State Space Model to capture long-range temporal dependencies and global spatial features, achieving high-accuracy performance on long sequences.
Physical field reconstruction (PFR) aims to predict the state distribution of physical quantities (e.g., velocity, pressure, and temperature) based on limited sensor measurements. It plays a critical role in domains such as fluid dynamics and thermodynamics. However, existing deep learning methods often fail to capture long-range temporal dependencies, resulting in suboptimal performance on time-evolving physical systems. To address this, we propose FR-Mamba, a novel spatiotemporal flow field reconstruction framework based on state space modeling. Specifically, we design a hybrid neural network architecture that combines Fourier Neural Operator (FNO) and State Space Model (SSM) to capture both global spatial features and long-range temporal dependencies. We adopt Mamba, a recently proposed efficient SSM architecture, to model long-range temporal dependencies with linear time complexity. In parallel, the FNO is employed to capture non-local spatial features by leveraging frequency-domain transformations. The spatiotemporal representations extracted by these two components are then fused to reconstruct the full-field distribution of the physical system. Extensive experiments demonstrate that our approach significantly outperforms existing PFR methods in flow field reconstruction tasks, achieving high-accuracy performance on long sequences.