If I may add some thoughts to @ali’s recommendations:
Adaptive PCE can help to get a smaller error, but take care not to choose a too large degree. The number of input dimensions in your problem is quite high. The number of basis functions in your total degree basis should not be too large, at most in the order of several ten thousands for an ED of size 1000. Of course, you can restrict the number of basis functions by setting the q-norm to something smaller than 1. This removes high-order interaction terms and allows you to use a higher degree. You could also try to directly restrict the interaction order to 2 or 3:
MetaOpts.TruncOptions.MaxInteraction = 2;
Often, the higher-order interaction terms are not so significant in physics-based models.
Regarding your question how to know whether your metamodel is “reliable”: it depends on what you mean with this, and what you want to use the metamodel for. As @bsudret said, the metamodel typically doesn’t need to be too accurate to yield good estimates for moments and sensitivity indices. LOO error and validation error measure how close the metamodel is to your original model. In your case, it has a validation error of 10%, so it is not that close. In that sense, it is not that “reliable” (for pointwise prediction).
Theorywise, if your model is in L^2_{f_{\bf X}} (i.e. has finite variance under the input distribution), you can approximate it to arbitrary accuracy with polynomials. But in practice, it might be that very many terms, high-order and high-degree, are needed to approximate the model accurately, which requires accordingly many points to identify the coefficients of these terms. Some models are not suited well to being approximated by polynomials, and then the PCE converges only slowly with increasing basis and number of points. The function f(x) = |x| is one example, or discontinuous functions.
So, if you increase the ED and allow for a larger basis, for sure you will get a better approximation - eventually. But it might be that very many points are needed to get a satisfactory approximation.
Regarding your first question: to me it looks like you achieved the maximal accuracy that you can get with the prescribed basis. After 700 points, you don’t gain much accuracy anymore. It might be that a larger basis (using basis adaptivity as recommended by Ali) would help here. The plot itself looks normal.
Good luck, and let us know how you continued with the analysis! 