Combining neural network based models with mechanistic models could potentially improve the prediction quality, reduce unexpected outcomes, help with lowering the training efforts and yield more interpretable, explainable and generalizable models. One mechanistic modeling is Systems Dynamics (SD) Modeling. Systems Dynamics Modeling has been used for analyzing complex social, managerial, economic, policy or ecological systems. Given that systems dynamics models could be very expressive, can we try to combine them with neural networks, and particularly, can we incorporate some known dynamics from the SD models into neural networks? This paper describes Systems Dynamics Aware Neural Networks that facilitate incorporating known dynamics of the system by following a very flexible programming model. Benefits include more accessible modeling, helping the practitioners when mathematical tools have limited applicability, and enabling modeling more complex behavior than physical systems. This paper argues that combining the expressive power of SD with neural networks opens up possibilities in economics, finance, operational research, policy making, corporate strategies and many complex problems.