Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction

Researchers introduce Physics-Encoded Inversion (PhysE-Inv), a deep learning framework combining LSTM networks with physics-informed guidance to improve snow depth estimation in Arctic regions. The method achieves 24.7% MSE reduction over baseline models by learning latent parameters from sparse observational data, demonstrating wider applicability for inverse modeling in data-scarce scientific domains.

PhysE-Inv addresses a fundamental computational challenge: solving inverse problems where observational data is limited and sparse, a constraint that applies across climate science, geophysics, and environmental monitoring. The framework's innovation lies in its hybrid approach, merging sequential deep learning with physics-informed constraints rather than relying on either methodology alone. By encoding domain knowledge directly into the model architecture through contrastive learning regularization, the system learns noise-invariant representations that capture meaningful temporal patterns in Arctic snow dynamics.

The research builds on growing recognition that purely data-driven models struggle in sparse-data regimes, while pure physics-based models lack flexibility to capture complex nonlinear behaviors. Physics-informed machine learning represents an emerging paradigm across scientific computing, with applications from fluid dynamics to materials science. Snow depth estimation holds particular relevance given climate change impacts on polar regions and the cascading effects on global weather systems and sea level rise.

The reported performance metrics—24.7% average improvement and 17.3% over the strongest baseline—suggest practical viability for deployment in remote Arctic monitoring systems. This capability could enhance climate models, improve seasonal forecasting, and inform polar research initiatives. The framework's generalizability to other data-scarce domains positions it as a transferable methodology rather than a single-use solution.

Future development should focus on validation against field measurements, integration with satellite observational networks, and extension to other geophysical inverse problems including ice sheet dynamics and subsurface characterization.

• →PhysE-Inv combines LSTM networks with physics-informed constraints to solve sparse-data inverse problems with 24.7% error reduction over baselines.
• →Contrastive learning regularization enables the model to learn noise-invariant representations crucial for real-world observational settings.
• →Physics-informed machine learning represents a generalizable paradigm for scientific domains where data scarcity and domain knowledge coexist.
• →Snow depth estimation improvements support climate modeling, Arctic monitoring, and seasonal weather forecasting applications.
• →The framework demonstrates transferability to other data-scarce inverse modeling challenges across geophysics and environmental science.