Probabilistic Downscaling for High-Resolution Weather Data

The patent creates a statistical copy of the complex CFD simulation process through trained neural network models. These models learn the mapping from coarse-resolution boundary conditions to fine-resolution physical parameters, enabling rapid generation of high-resolution data without re-running expensive CFD simulations. The neural networks are trained once on CFD data and then serve as efficient surrogates for repeated predictions.

The patent performs preliminary action by pre-training the neural network models offline using comprehensive CFD simulation data. This training phase captures the complex physical relationships in advance, allowing the models to quickly predict fine-resolution parameters when deployed. The heavy computational lifting is done beforehand, enabling fast real-time or near-real-time predictions during actual use.

The patent replaces the complex CFD simulation engine with simplified neural network copies that capture essential physical relationships. These neural networks have significantly fewer computational requirements and can be deployed on edge devices with limited resources. The models maintain accuracy by learning from comprehensive CFD training data while using simplified architectural structures suitable for edge deployment.

The patent substitutes the mechanical CFD simulation process (solving partial differential equations numerically) with a data-driven neural network system. This substitution replaces complex iterative numerical computations with direct neural network inference, dramatically reducing computational complexity and enabling deployment on resource-constrained edge devices while maintaining predictive capability.

The patent creates statistical copies of the data assimilation process through neural networks trained on CFD simulation data. These neural network copies rapidly predict fine-resolution physical parameters from coarse-resolution inputs without performing iterative data assimilation computations. The training phase captures the assimilation relationships, enabling fast inference that maintains accuracy while dramatically improving processing speed.

The patent performs preliminary data assimilation during the offline training phase, where neural networks learn the optimal mapping from boundary conditions to physical parameters using comprehensive CFD data. This preliminary learning enables the models to quickly make accurate predictions during deployment without performing repeated data assimilation iterations, thus maintaining accuracy while improving productivity.

The patent creates local copies of the simulation capability through edge-deployable neural network models. These models run directly on local devices without requiring continuous cloud connectivity, eliminating data transmission delays. The neural networks are trained on comprehensive CFD data and then serve as standalone prediction systems that provide accurate results immediately without cloud round-trips.

The patent performs preliminary model training and parameter optimization offline before deployment in harsh conditions. This preliminary preparation enables the edge devices to autonomously perform accurate predictions without needing to communicate with cloud systems during operation, thus eliminating data transmission time delays while maintaining simulation accuracy.