  The robotic systems continuously interact with complex dynamical systems in
the physical world. Reliable predictions of spatiotemporal evolution of these
dynamical systems, with limited knowledge of system dynamics, are crucial for
autonomous operation. In this paper, we present HybridNet, a framework that
integrates data-driven deep learning and model-driven computation to reliably
predict spatiotemporal evolution of a dynamical systems even with in-exact
knowledge of their parameters. A data-driven deep neural network (DNN) with
Convolutional LSTM (ConvLSTM) as the backbone is employed to predict the
time-varying evolution of the external forces/perturbations. On the other hand,
the model-driven computation is performed using Cellular Neural Network (CeNN),
a neuro-inspired algorithm to model dynamical systems defined by coupled
partial differential equations (PDEs). CeNN converts the intricate numerical
computation into a series of convolution operations, enabling a trainable PDE
solver. With a feedback control loop, HybridNet can learn the physical
parameters governing the system's dynamics in real-time, and accordingly adapt
the computation models to enhance prediction accuracy for time-evolving
dynamical systems. The experimental results on two dynamical systems, namely,
heat convection-diffusion system, and fluid dynamical system, demonstrate that
the HybridNet produces higher accuracy than the state-of-the-art deep learning
based approach.
