Unlocking Out-of-Distribution Generalization in Dynamics through Physics-Guided Augmentation

• URL: http://arxiv.org/abs/2510.24216v1
• Date: Tue, 28 Oct 2025 09:30:35 GMT
• Title: Unlocking Out-of-Distribution Generalization in Dynamics through Physics-Guided Augmentation
• Authors: Fan Xu, Hao Wu, Kun Wang, Nan Wang, Qingsong Wen, Xian Wu, Wei Gong, Xibin Zhao,
• Abstract summary: We present SPARK, a physics-guided quantitative augmentation plugin.<n>Experiments on diverse benchmarks demonstrate that SPARK significantly outperforms state-of-the-art baselines.
• Score: 46.40087254928057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In dynamical system modeling, traditional numerical methods are limited by high computational costs, while modern data-driven approaches struggle with data scarcity and distribution shifts. To address these fundamental limitations, we first propose SPARK, a physics-guided quantitative augmentation plugin. Specifically, SPARK utilizes a reconstruction autoencoder to integrate physical parameters into a physics-rich discrete state dictionary. This state dictionary then acts as a structured dictionary of physical states, enabling the creation of new, physically-plausible training samples via principled interpolation in the latent space. Further, for downstream prediction, these augmented representations are seamlessly integrated with a Fourier-enhanced Graph ODE, a combination designed to robustly model the enriched data distribution while capturing long-term temporal dependencies. Extensive experiments on diverse benchmarks demonstrate that SPARK significantly outperforms state-of-the-art baselines, particularly in challenging out-of-distribution scenarios and data-scarce regimes, proving the efficacy of our physics-guided augmentation paradigm.

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