PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction

2026-01-23

AI Summary

This study addresses the challenge of retrieving Arctic snow depth from sparse and noisy sea ice parameter observations, where existing methods often suffer from high data sensitivity or limited physical interpretability. To overcome these limitations, this work proposes a physics-constrained sequential inversion framework that integrates an LSTM encoder–decoder architecture with a multi-head attention mechanism. The approach incorporates a proxy objective derived from a hydrostatic-equilibrium-based forward model and enforces physical regularization in the latent space, further enhanced by physics-guided contrastive learning. Notably, the method enables dynamic and interpretable inverse modeling without requiring direct snow depth ground truth. Experimental results demonstrate a 20% reduction in prediction error compared to state-of-the-art approaches, along with significantly improved physical consistency and robustness under sparse and noisy observational conditions.

Technology Category

• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

The accurate estimation of Arctic snow depth ($h_s$) 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 inference.Our 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 against state-of-the-art baselines, PhysE-Inv significantly improves prediction performance, reducing error by 20\% while demonstrating superior physical consistency and resilience to data sparsity compared to empirical methods. This approach pioneers a path for noise-tolerant, interpretable inverse modeling, with wide applicability in geospatial and cryospheric domains.

Problem

• Arctic snow depth
• inverse problem
• data sparsity
• physical interpretability
• sea ice parameters

Innovation

• physics-informed inverse modeling
• snow depth prediction
• latent space regularization
• hydrostatic balance proxy
• contrastive learning