Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910274694414336 |
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| author | Sampath, Akila Janeja, Vandana Wang, Jianwu |
| author_facet | Sampath, Akila Janeja, Vandana Wang, Jianwu |
| contents | Accurate estimation in time-varying inverse problems under limited and sparse observations remains a fundamental challenge across scientific domains. For example, snow depth estimation requires inferring hidden parameters governing sea ice physics, which can be incorporated through physics-informed encoding. To address this challenge, we introduce Physics-Encoded Inversion (PhysE-Inv), a novel framework that combines deep sequential learning with physics-informed inference for solving inverse problems under real-world sparse observational settings. PhysE-Inv integrates an LSTM encoder-decoder to capture temporal dependencies, together with contrastive learning regularization that enforces noise-invariant latent representations. The framework learns latent parameters that, when combined with observational inputs, reconstruct snow depth while incorporating physics-informed guidance. PhysE-Inv consistently outperforms all evaluated baselines, achieving an average MSE reduction of 24.7\% across all baseline models and a 17.3\% improvement over the strongest baseline under parameter estimation settings. Overall, our work demonstrates a generalizable inverse modeling paradigm for data-scarce domains where physics-informed guidance can be incorporated into sparse observations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17074 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction Sampath, Akila Janeja, Vandana Wang, Jianwu Machine Learning Artificial Intelligence Accurate estimation in time-varying inverse problems under limited and sparse observations remains a fundamental challenge across scientific domains. For example, snow depth estimation requires inferring hidden parameters governing sea ice physics, which can be incorporated through physics-informed encoding. To address this challenge, we introduce Physics-Encoded Inversion (PhysE-Inv), a novel framework that combines deep sequential learning with physics-informed inference for solving inverse problems under real-world sparse observational settings. PhysE-Inv integrates an LSTM encoder-decoder to capture temporal dependencies, together with contrastive learning regularization that enforces noise-invariant latent representations. The framework learns latent parameters that, when combined with observational inputs, reconstruct snow depth while incorporating physics-informed guidance. PhysE-Inv consistently outperforms all evaluated baselines, achieving an average MSE reduction of 24.7\% across all baseline models and a 17.3\% improvement over the strongest baseline under parameter estimation settings. Overall, our work demonstrates a generalizable inverse modeling paradigm for data-scarce domains where physics-informed guidance can be incorporated into sparse observations. |
| title | Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.17074 |