Physics-regulated dynamic mode decomposition for natural gas pipeline flow

This study introduces a novel integration of dynamic mode decomposition (DMD) with physical regulations for natural gas pipeline flow. It aims to address the limitations of purely data-driven models and the importance of incorporating the physics of complex dynamic systems. By considering the mass conservation law, the proposed model ensures that the predictions generated via DMD with control adhere to the physical laws, resulting in a multi-objective optimization problem. To verify its performance, the proposed model was evaluated using real-world data of natural gas pipelines. The results demonstrate its superior accuracy and ability to avoid physically implausible predictions, particularly under data-limited conditions. Despite an increase in the overall computational cost by approximately 15%, the model achieved up to 50% error reduction with scarce training data, highlighting its robustness and effectiveness. This study represents a significant advancement in data-driven modeling techniques by fulfilling the critical need for accurate and reliable predictions that respect physical constraints, thus enhancing the interpretability and validity of the results.