Systematic design space exploration by learning the explored space using Machine Learning
This work addresses the issue of inefficient exploration in parameter spaces for researchers and engineers, though it appears incremental as it builds on existing methods like robust random-cut forests.
The paper tackles the problem of parameter space exploration by using machine learning to track explored regions and sample from unexplored ones, demonstrating the method in two-dimensional Euclidean space with potential extension to any dimension.
Current practice in parameter space exploration in euclidean space is dominated by randomized sampling or design of experiment methods. The biggest issue with these methods is not keeping track of what part of parameter space has been explored and what has not. In this context, we utilize the geometric learning of explored data space using modern machine learning methods to keep track of already explored regions and samples from the regions that are unexplored. For this purpose, we use a modified version of a robust random-cut forest along with other heuristic-based approaches. We demonstrate our method and its progression in two-dimensional Euclidean space but it can be extended to any dimension since the underlying method is generic.