Abstract:
This research compares the performance of space-time surrogate models (STSMs) and
network surrogate models (NSMs). Specifically, when the system response varies over time (or
pseudo-time), the surrogates must predict the system response. A surrogate model is used to
approximate the response of computationally expensive spatial and temporal fields resulting from
some computational mechanics simulations. Within a design context, a surrogate takes a vector
of design variables that describe a current design and returns an approximation of the design’s
response through a pseudo-time variable. To compare various radial basis function (RBF) surrogate
modeling approaches, the prediction of a load displacement path of a snap-through structure is used
as an example numerical problem. This work specifically considers the scenario where analytical
sensitivities are available directly from the computational mechanics’ solver and therefore gradient
enhanced surrogates are constructed. In addition, the gradients are used to perform a domain
transformation preprocessing step to construct surrogate models in a more isotropic domain, which is
conducive to RBFs. This work demonstrates that although the gradient-based domain transformation
scheme offers a significant improvement to the performance of the space-time surrogate models
(STSMs), the network surrogate model (NSM) is far more robust. This research offers explanations
for the improved performance of NSMs over STSMs and recommends future research to improve the
performance of STSMs.
