The problem characteristics that affect the choice of MOR technique include the model structure, the input and output variables, the parameter variations, the frequency range, and the accuracy requirements. For example, if your model is linear and time-invariant, you can use projection-based techniques such as balanced truncation or Krylov methods. If your model is nonlinear or time-varying, you can use data-driven techniques such as proper orthogonal decomposition or neural networks. If your input and output variables are fixed, you can use local MOR techniques that focus on a specific input-output pair. If your input and output variables are variable, you can use global MOR techniques that cover a range of input-output pairs. If your model parameters are constant, you can use parametric MOR techniques that generate a single reduced model. If your model parameters are variable, you can use non-parametric MOR techniques that generate multiple reduced models. If your frequency range is low, you can use low-order MOR techniques that capture the dominant dynamics. If your frequency range is high, you can use high-order MOR techniques that capture the higher-order dynamics. If your accuracy requirements are low, you can use coarse MOR techniques that sacrifice some accuracy for speed. If your accuracy requirements are high, you can use fine MOR techniques that preserve more accuracy but take longer.