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The robotic systems continuously interact with complex dynamical systems inthe physical world. Reliable predictions of spatiotemporal evolution of thesedynamical systems, with limited knowledge of system dynamics, are crucial forautonomous operation. In this paper, we present HybridNet, a framework thatintegrates data-driven deep learning and model-driven computation to reliablypredict spatiotemporal evolution of a dynamical systems even with in-exactknowledge of their parameters. A data-driven deep neural network (DNN) withConvolutional LSTM (ConvLSTM) as the backbone is employed to predict thetime-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 coupledpartial differential equations (PDEs). CeNN converts the intricate numericalcomputation into a series of convolution operations, enabling a trainable PDEsolver. With a feedback control loop, HybridNet can learn the physicalparameters governing the system's dynamics in real-time, and accordingly adaptthe computation models to enhance prediction accuracy for time-evolvingdynamical systems. The experimental results on two dynamical systems, namely,heat convection-diffusion system, and fluid dynamical system, demonstrate thatthe HybridNet produces higher accuracy than the state-of-the-art deep learningbased approach.

HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems