Water Infrastructure Control Scheduling With ML Model Copying

The patent creates a simplified copy of the complex physical model by training a machine learning model (neural network) on data generated from the traditional physically-based numerical model. This ML copy reproduces the behavior of the complex model with much lower computational requirements, enabling real-time applications while maintaining accuracy.

The patent replaces the mechanical/computational system of traditional numerical models with a machine learning-based system. The complex differential equations and iterative calculations are substituted with a trained neural network that provides rapid predictions, eliminating the computational burden while preserving model functionality.

The patent performs preliminary action by training the machine learning model offline using data from traditional numerical simulations. Once trained, the model can rapidly predict system behavior without requiring real-time computational resources, thus reducing execution time while maintaining accuracy.

A simplified copy of the complex simulation model is created through machine learning. This copy reproduces the system characteristics accurately but executes in seconds rather than hours, enabling real-time control and decision-making applications.

The machine learning model is trained automatically on historical data without requiring continuous expert intervention. Once deployed, it maintains itself with minimal staff resources, eliminating the need for specialized personnel to continuously calibrate and maintain complex physical models.

The manual processes of model calibration, validation, and maintenance are replaced with automated machine learning techniques. The system self-adjusts and requires significantly fewer human resources to operate and maintain while preserving comprehensive modeling capabilities.