Dimensionless Variable

Dimensionless variable analysis is a mathematical technique used to simplify complex systems by identifying key relationships between variables independent of their units. Current research focuses on applying this approach to diverse fields, including multi-robot systems performance modeling, improving the efficiency of machine learning algorithms (e.g., through symbolic regression and deep learning models), and enhancing the interpretability of neural networks. This methodology offers significant advantages by reducing model complexity, improving prediction accuracy (especially in extrapolation), and facilitating knowledge transfer between similar systems, ultimately leading to more efficient and robust scientific modeling and engineering design.