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A mechanistically informed data-driven approach is proposed to simulate the complex plastic behavior of microstructured/homogenized solids subjected to cyclic loading, especially to simulate the Masing effect. Our proposed approach avoids the complicated mathematical construction of an appropriate yield surface, and does not require a large amount of data for training, by virtue of its mechanistic character, which couples the methods and tools of data science to the principles of mechanics. Specifically, a data-processing method is herein advanced to extract specific internal variables that characterize cyclic plastic behavior, which cannot be measured directly via physical experiments. A yield surface, represented by an artificial neural network (ANN), is then trained by stress–strain data and the extracted internal variables. Finally, the ANN is integrated into a finite element computational framework to solve …