Simplified Modeling

The current challenges posed to engineers by the many consequences of climate change and the aim to achieve sustainable development goals drive the need to model both natural and artificial phenomena of increasing complexity in ways that remain tractable, interpretable, computationally efficient, and suitable to iterative optimization and design schemes. This work briefly reviews the techniques that are most commonly used for model simplification in the field of engineering. It mainly covers closed-form analytical models, numerical model simplification approaches, regression-based models, machine learning models, and prominent model order reduction techniques.