Model reduction is a research area in the field of control engineering and computational mathematics that focuses on simplifying complex mathematical models without losing their essential characteristics. This simplification is done in order to reduce the computational cost of simulations and analysis, making it easier to study and make decisions based on these models. Model reduction techniques can include methods such as truncation, projection, and system order reduction, among others. By reducing the complexity of models, researchers can gain insights into system behavior, design more efficient control strategies, and improve computational performance.