Before revision:  In this work, we aim to study systematic generalization in predicting future state trajectories of a dynamical system, conditioned on past states' trajectory (dependent variables), past and future actions (control variables). In our context, systematic generalization implies that a good model should perform well on all new combinations of future actions after being trained on all of them, but only on a limited set of their combinations.
After revision:  In this work, we aim to study systematic generalization in predicting future state trajectories of a dynamical system, conditioned on past states' trajectory (dependent variables), past and future control variables. We focus on systematic generalization wherein the NN-based forecasting model should perform well on all new combinations of future actions after being trained on all of them, but only on a limited set of their combinations.