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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.24216 |
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| _version_ | 1866915582588223488 |
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| author | Xu, Fan Wu, Hao Wang, Kun Wang, Nan Wen, Qingsong Wu, Xian Gong, Wei Zhao, Xibin |
| author_facet | Xu, Fan Wu, Hao Wang, Kun Wang, Nan Wen, Qingsong Wu, Xian Gong, Wei Zhao, Xibin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24216 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unlocking Out-of-Distribution Generalization in Dynamics through Physics-Guided Augmentation Xu, Fan Wu, Hao Wang, Kun Wang, Nan Wen, Qingsong Wu, Xian Gong, Wei Zhao, Xibin Machine Learning 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. |
| title | Unlocking Out-of-Distribution Generalization in Dynamics through Physics-Guided Augmentation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.24216 |