A Dynamically Controlled Recurrent Neural Network for Modeling Dynamical Systems

Understand

This work proposes a novel neural network architecture, called the Dynamically Controlled Recurrent Neural Network (DCRNN), specifically designed to model dynamical systems that are governed by ordinary differential equations (ODEs).

• The current state vectors of these types of dynamical systems only depend on their state-space models, along with the respective inputs and initial conditions.
• Long Short-Term Memory (LSTM) networks, which have proven to be very effective for memory-based tasks, may fail to model physical processes as they tend to memorize, rather than learn how to capture the information on the underlying dynamics.
• The proposed DCRNN includes learnable skip-connections across previously hidden states, and introduces a regularization term in the loss function by relying on Lyapunov stability theory.