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Current practice in parameter space exploration in euclidean space isdominated by randomized sampling or design of experiment methods. The biggestissue with these methods is not keeping track of what part of parameter spacehas been explored and what has not. In this context, we utilize the geometriclearning of explored data space using modern machine learning methods to keeptrack of already explored regions and samples from the regions that areunexplored. For this purpose, we use a modified version of a robust random-cutforest along with other heuristic-based approaches. We demonstrate our methodand its progression in two-dimensional Euclidean space but it can be extendedto any dimension since the underlying method is generic.

Systematic design space exploration by learning the explored space using Machine Learning