Despite their recent success, machine learning (ML) models such as graph neural networks (GNNs), suffer from drawbacks the need for large training datasets and poor performance unseen cases. In this work, we use transfer (TL) approaches to circumvent retraining with datasets. We apply TL an existing ML framework, trained predict multiple crack propagation stress evolution in brittle materials u...