We present the results of the application of the active learning method in developing surrogates as physics‐based earthquake ground‐motion simulators. The surrogates, which map input parameters into output values without demanding intensive computations, are an essential part of any parameter optimization, sensitivity, and uncertainty analysis. Artificial neural networks (ANNs), as an example of surrogates, are very effective in estimating any complicated model. ANNs use a set of training data to learn the mapping process. Training data are a set of input parameters and their corresponding output values. Generating training data requires conducting numerous regional scale ground‐motion simulations. These numerical simulations are computationally challenging. Therefore, a step‐by‐step learning method should be employed to reduce the need for generating unnecessary training data. These methods are called active learning. In this study, we use a pool‐based query by committee (QBC) active learning method with effective initialization approach to study the performance of the models in the training process. We use a dataset that is generated for a moderate earthquake on a regional scale for anelastic attenuation studies with the focus on the estimation of peak ground velocity. The results show that active learning provides better performance in reducing generalization error than does passive learning while the same number of training data is used. Variation of performance with an increasing number of training data is significantly less in an active learning approach which indicates its stable and predictable behavior. This study, although limited to one earthquake and a metric, indicates that in developing surrogates as physics‐based earthquake ground‐motion simulators, application of active learning is an important step in reducing computational demands and generating stable predictors.

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