  We present a novel paradigm for modeling certain types of dynamic simulation
in real-time with the aid of neural networks. In order to significantly reduce
the requirements on data (especially time-dependent data), as well as decrease
generalization error, our approach utilizes a data-driven neural network only
to capture quasistatic information (instead of dynamic or time-dependent
information). Subsequently, we augment our quasistatic neural network (QNN)
inference with a (real-time) dynamic simulation layer. Our key insight is that
the dynamic modes lost when using a QNN approximation can be captured with a
quite simple (and decoupled) zero-restlength spring model, which can be
integrated analytically (as opposed to numerically) and thus has no time-step
stability restrictions. Additionally, we demonstrate that the spring
constitutive parameters can be robustly learned from a surprisingly small
amount of dynamic simulation data. Although we illustrate the efficacy of our
approach by considering soft-tissue dynamics on animated human bodies, the
paradigm is extensible to many different simulation frameworks.
