Applying Machine Learning to Deterministic Pump-Pipe System

The understanding of ML fundamentals is also useful to apply it to well-defined deterministic systems where results can be validated.

Machine learning (ML) is widely used to model complex, non-linear relationships in data, especially when dealing with uncertainties or measurement errors. However, to develop an understanding of ML fundamentals, it is also useful to apply it to well-defined deterministic systems where results can be validated against known physical models. This document outlines a structured workflow where ML is applied to a simulated centrifugal pump and pipeline system. The goal is to train a neural network using synthetic data and verify its predictive capability against the original simulation results.