On applying optimal design of experiments when functional observations occur

In this work we study the theory of optimal design of experiments when functional observations occur. We provide the best estimate for the functional coefficient in a linear model with functional response and multivariate predictor, exploiting fully the information provided by both functions and derivatives. We define different optimality criteria for the estimate of a functional coefficient. Then, we provide a strong theoretical foundation to prove that the computation of these optimal designs, in the case of linear models, is the same as in the classical theory, but a different interpretation needs to be given.