Computational models provide mathematical abstractions of real-world problems and are typically used to understand the behavior of a physical system, formulate hypotheses, and make predictions, while reducing the economical and ethical cost of experiments. In the field of neuromodulation, modeling the effects of neural stimulation is a fundamental step in the design of neuroprosthetic devices. Currently, the so-called hybrid models (HMs), which encompass the problems of volume conduction and neural response computation, have been successfully employed in the context of spinal cord stimulation, deep brain stimulation and peripheral nerve stimulation. At the state of the art, the main weakness of HMs is their high computational cost and the consequent difficulty of parameter optimizations requiring many model evaluations.