I have a very basic understanding of Gaussian Processes. From what I understand, a Gaussian process for a set $X$, is the assignment of a Gaussian distribution to every element of the set. This is meant to expand the idea of a function to the case where we don't have total information about a function.
We can define the entropy of a probability distribution $p(x)$ as follows:
$$S = \int_X p(x) \log p(x) $$
with $x \in X$. How do we compute the entropy of a Gaussian Process?