arXiv AI
Physics-Knowledge-Guided Hybrid Neural Learning for Arctic Sea Ice Concentration Evolution and Short-Range Prediction
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arXiv AI
Jul 24
Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics
arXiv:2601. 17647v3 Announce Type: replace-cross Abstract: Quantifying the causal relationship between sea ice thickness and sea surface height (SSH) is essential for understanding the mechanisms driving polar climate dynamics.
arXiv AI
1d ago
Physical knowledge on historical data matters more than enforcing physical constraints on the forecast
The paper introduces a Physics Informed Recurrent Neural Network (PIRNN) that simultaneously predicts target time series and unobservable intermediate physical variables, enhancing robustness and interpretability. It adapts to any physical model with multiple equations and variables, demonstrated on groundwater level predictions using the Gardenia model. Experiments on twelve real‑world datasets show PIRNN outperforming several neural network baselines and the Gardenia model, with an ablation study confirming the value of physical knowledge.
arXiv Machine Learning
Jun 17
Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific
arXiv:2606. 17659v1 Announce Type: new Abstract: This study introduces enhancements to physics-constrained neural networks (PCNNs) that improve the accuracy and stability of hybrid short-term weather forecasting models.
arXiv AI
Sep 10
Neptune: An AI model for Global Ocean Subseasonal Prediction
Neptune is an end‑to‑end data‑driven framework that emulates global ocean and sea‑ice states for subseasonal‑to‑seasonal (S2S) forecasting up to 60 days. It combines Convolutional Neural Networks and Spherical Fourier Neural Operators to capture both local features and global cross‑scale interactions, producing daily outputs for temperature, salinity, currents, sea‑surface height, and sea‑ice metrics at 1° and 0.25° resolution. Evaluated against metrics such as RMSE, CRPS, ACC, and climate indices (ENSO, IOD), Neptune reproduces the spatio‑temporal evolution of oceanic fields and remains stable over long timescales.
Hugging Face Trending Papers
Sep 8
Neptune: An AI model for Global Ocean Subseasonal Prediction
Subseasonal-to-seasonal (S2S) forecasting is societally critical, supporting decision-making in sectors ranging from water and agricultural management to disaster risk reduction, energy planning, and...
arXiv Machine Learning
5d ago
Physics-enriched neural solvers for transient ice-flow simulation
Physics-enriched neural solvers for transient ice-flow simulation present a method where a neural network represents the glacier velocity field, warm-started from the previous time step and updated with few optimizer iterations. By feeding the network inexpensive input fields derived from low-order ice-flow balances, the solver improves robustness and accuracy across three real-world glacier configurations, achieving surface-velocity errors reduced by factors of two to four at fixed runtime. The approach enables a 300-year Aletsch simulation to finish in under one minute on a single GPU, demonstrating significant computational savings compared to traditional higher-order models.