  Advances in predictive modeling across multiple disciplines have yielded
generative models capable of high veracity in predicting macroscopic functional
responses of materials. Correspondingly, of interest is the inverse problem of
finding the model parameter that will yield desired macroscopic responses, such
as stress-strain curves, ferroelectric hysteresis loops, etc. Here we suggest
and implement a Gaussian Process based methods that allow to effectively sample
the degenerate parameter space of a complex non-local model to output regions
of parameter space which yield desired functionalities. We discuss the specific
adaptation of the acquisition function and sampling function to make the
process efficient and balance the efficient exploration of parameter space for
multiple possible minima and exploitation to densely sample the regions of
interest where target behaviors are optimized. This approach is illustrated via
the hysteresis loop engineering in ferroelectric materials, but can be adapted
to other functionalities and generative models. The code is open-sourced and
available at [github.com/ramav87/Ferrosim].
