Optimal Data Generation in Multi-Dimensional Parameter Spaces, using
  Bayesian Optimization
M. R. Mahani Igor A. Nechepurenko Yasmin Rahimof Andreas Wicht
Acquiring a substantial number of data points for training accurate machine
learning (ML) models is a big challenge in scientific fields where data
collection is resource-intensive. Here, we propose a novel approach for
constructing a minimal yet highly informative database for training ML models
in complex multi-dimensional parameter spaces. To achieve this, we mimic the
underlying relation between the output and input parameters using Gaussian
process regression (GPR). Using a set of known data, GPR provides predictive
means and standard deviation for the unknown data. Given the predicted standard
deviation by GPR, we select data points using Bayesian optimization to obtain
an efficient database for training ML models. We compare the performance of ML
models trained on databases obtained through this method, with databases
obtained using traditional approaches. Our results demonstrate that the ML
models trained on the database obtained using Bayesian optimization approach
consistently outperform the other two databases, achieving high accuracy with a
significantly smaller number of data points. Our work contributes to the
resource-efficient collection of data in high-dimensional complex parameter
spaces, to achieve high precision machine learning predictions.