The following three points are drawbacks 1 There is no discussion or theoretical analysis of why the proposed method is effective. Therefore, I cannot eliminate the suspicion that the effectiveness of the proposed method over the prior methods depends on the superiority of the hyperparameter tuning. I think it is good to show what kind of "prior physics knowledge in nonlinear dynamics" can be implemented by the proposed algorithm, and provide numerical experiments to demonstrate its effectiveness.
2 Where the stability of the estimation results due to hyperparameter settings is unclear. Please explain the optimization method for hyperparameters and the stability of the estimation results with respect to variations in hyperparameters.
3 Where it is unclear whether the hyperparameter tuning of the prior method is appropriate. It is same as above point. In particular, we would like to know why the results in Table 1 do not match the results of similar experiments presented in the paper proposing the prior method. (Table 1 in [Mundhenk et al., 2021])
3 Where it is unclear whether the hyperparameter tuning of the prior method is appropriate. It is same as above point. In particular, we would like to know why the results in Table 1 do not match the results of similar experiments presented in the paper proposing the prior method. (Table 1 in [Mundhenk et al., 2021])