To circumvent the above issue, AL could be used as a strategic approach to optimize the data acquisition process. AL, a subfield of ML, reduces the data burden to train a model through an iterative effort that guides the collection of training data only towards the most informative data points, while simultaneously using these data points to train a surrogate model to predict the quantity of interest and the uncertainty associated with the prediction. In this work, we will add data to the training using the points for which the prediction uncertainty is highest.