Can Transformers overcome the lack of data in the simulation of history-dependent flows?

This article explores the application of Transformers in simulating history-dependent flows, specifically addressing the challenge of limited data. The research likely investigates the ability of Transformers to generalize and learn from sparse data in this domain. The focus is on the potential of Transformers to improve the accuracy and efficiency of simulations where past events significantly influence current states.