Modeling unknown dynamical systems with hidden parameters
This addresses the challenge of predicting system behavior when parameters are completely hidden, which is incremental as it builds on existing data-driven methods.
The authors tackled the problem of modeling unknown dynamical systems with hidden parameters by training a deep neural network using trajectory data, achieving accurate predictions for new initial conditions and unknown parameters over longer time periods.
We present a data-driven numerical approach for modeling unknown dynamical systems with missing/hidden parameters. The method is based on training a deep neural network (DNN) model for the unknown system using its trajectory data. A key feature is that the unknown dynamical system contains system parameters that are completely hidden, in the sense that no information about the parameters is available through either the measurement trajectory data or our prior knowledge of the system. We demonstrate that by training a DNN using the trajectory data with sufficient time history, the resulting DNN model can accurately model the unknown dynamical system. For new initial conditions associated with new, and unknown, system parameters, the DNN model can produce accurate system predictions over longer time.