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

arXiv: https://arxiv.org/abs/2303.08249 · HTML: https://mlsanity.com/paper/2303.08249/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [Rare event failure test case generation in Learning-Enabled-Controllers](https://mlsanity.com/paper/2206.05533/) (2022)
- [Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression](https://mlsanity.com/paper/2211.10360/) (2022)
- [Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design](https://mlsanity.com/paper/2302.14732/) (2023)
- [Search for universal minimum drag resistance underwater vehicle hull using CFD](https://mlsanity.com/paper/2302.09441/) (2023)
- [Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models](https://mlsanity.com/paper/2206.09247/) (2022)
- [Modeling and Optimization of a Longitudinally-Distributed Global Solar Grid](https://mlsanity.com/paper/2206.05584/) (2022)
- [Fusion of ML with numerical simulation for optimized propeller design](https://mlsanity.com/paper/2302.14740/) (2023)
- [Web-Based Platform for Evaluation of Resilient and Transactive Smart-Grids](https://mlsanity.com/paper/2206.05550/) (2022)
- [Malaria detection using Deep Convolution Neural Network](https://mlsanity.com/paper/2303.03397/) (2023)

## Abstract (arXiv)

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.