Many approaches to analog performance parameter macro modeling have been investigated by the research community. These models are typically derived from discrete data obtained from circuit simulation using numerous input combinations of component sizes for a given circuit topology. The simulations are computationally intensive, therefore it is advantageous to reduce the number of simulations necessary to build an accurate macro model. We present a new algorithm for adaptively sampling multi-dimensional black box functions based on Duchon pseudo-cubic splines. The splines readily and accurately model high dimensional functions based on discrete unstructured data and require no tuning of parameters as seen in many other interpolation methods. The adaptive sampler, in conjunction with pseudo-cubic splines, is used to accurately model various analog performance parameters for an operational amplifier topology using fewer sample points than traditional gridded and quasi-random sampling methodologies.