Technology Category
Intelligent Robots: State EstimationReasoning under Uncertainty: Relational Probabilistic ModelsPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environments
🤖 AI Summary
Addressing the challenge of long-term dynamic forecasting under high noise and irregular sampling, this paper proposes Phy-SSM, a generalized state-space model incorporating partial physical knowledge. Methodologically, it decouples prior physical laws into known and unknown state matrices embedded within the SSM architecture, and introduces a physics-informed state regularization term to enforce latent states to satisfy underlying dynamical constraints—thereby enhancing generalizability and solution uniqueness. Coupled with temporal decomposition and a robust optimization framework, Phy-SSM is specifically designed for sparse and noisy time-series data. Evaluated on three real-world tasks—vehicle motion prediction, UAV state estimation, and epidemic spread modeling—Phy-SSM achieves significant improvements over state-of-the-art baselines in both long-term interpolation and extrapolation, demonstrating superior accuracy and robustness.
Technology Category
Application Category
📝 Abstract
This work aims to address the problem of long-term dynamic forecasting in complex environments where data are noisy and irregularly sampled. While recent studies have introduced some methods to improve prediction performance, these approaches still face a significant challenge in handling long-term extrapolation tasks under such complex scenarios. To overcome this challenge, we propose Phy-SSM, a generalizable method that integrates partial physics knowledge into state space models (SSMs) for long-term dynamics forecasting in complex environments. Our motivation is that SSMs can effectively capture long-range dependencies in sequential data and model continuous dynamical systems, while the incorporation of physics knowledge improves generalization ability. The key challenge lies in how to seamlessly incorporate partially known physics into SSMs. To achieve this, we decompose partially known system dynamics into known and unknown state matrices, which are integrated into a Phy-SSM unit. To further enhance long-term prediction performance, we introduce a physics state regularization term to make the estimated latent states align with system dynamics. Besides, we theoretically analyze the uniqueness of the solutions for our method. Extensive experiments on three real-world applications, including vehicle motion prediction, drone state prediction, and COVID-19 epidemiology forecasting, demonstrate the superior performance of Phy-SSM over the baselines in both long-term interpolation and extrapolation tasks. The code is available at https://github.com/511205787/Phy_SSM-ICML2025.
Problem
Research questions and friction points this paper is trying to address.
Long-term dynamic forecasting in noisy, irregularly sampled environments
Integrating partial physics knowledge into state space models
Improving generalization for long-term interpolation and extrapolation tasks
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates physics knowledge into state space models
Decomposes system dynamics into known and unknown matrices
Uses physics state regularization for alignment
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2024-05-08Journal of Physics: Conference SeriesCitations: 4
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