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[2601.17074] PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction
Summary
The paper introduces PhysE-Inv, a novel physics-encoded inverse modeling framework designed to improve Arctic snow depth prediction by integrating machine learning with physical principles.
Why It Matters
Accurate snow depth estimation in the Arctic is crucial for climate modeling and understanding environmental changes. PhysE-Inv addresses limitations in existing models by enhancing prediction accuracy and interpretability, which is vital for climate-critical applications.
Key Takeaways
- PhysE-Inv combines LSTM architecture with physics-guided learning for better predictions.
- The framework reduces prediction error by 20% compared to state-of-the-art methods.
- It offers improved resilience to data sparsity and enhances physical consistency in predictions.
- The approach is applicable in various geospatial and cryospheric contexts.
- PhysE-Inv sets a precedent for integrating physical models with machine learning in environmental science.
Computer Science > Machine Learning arXiv:2601.17074 (cs) [Submitted on 23 Jan 2026 (v1), last revised 20 Feb 2026 (this version, v2)] Title:PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction Authors:Akila Sampath, Vandana Janeja, Jianwu Wang View a PDF of the paper titled PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction, by Akila Sampath and 2 other authors View PDF HTML (experimental) Abstract:The accurate estimation of Arctic snow depth remains a critical time-varying inverse problem due to the extreme scarcity and noise inherent in associated sea ice parameters. Existing process-based and data-driven models are either highly sensitive to sparse data or lack the physical interpretability required for climate-critical applications. To address this gap, we introduce PhysE-Inv, a novel framework that integrates a sophisticated sequential architecture, an LSTM Encoder-Decoder with Multi-head Attention and physics-guided contrastive learning, with physics-guided this http URL core innovation lies in a surjective, physics-constrained inversion methodology. This methodology first leverages the hydrostatic balance forward model as a target-formulation proxy, enabling effective learning in the absence of direct $h_s$ ground truth; second, it uses reconstruction physics regularization over a latent space to dynamically discover hidden physical parameters from noisy, incomplete time-series input. Evaluated...