Modeling unknown dynamical systems with hidden parameters

TL;DR

This paper introduces a data-driven neural network approach to model unknown dynamical systems with completely hidden parameters, enabling accurate long-term predictions from trajectory data without prior parameter knowledge.

Contribution

The authors develop a neural network-based method that can learn and predict unknown dynamical systems with hidden parameters solely from trajectory data.

Findings

Neural network models accurately capture system dynamics.

Method generalizes to new initial conditions with unseen parameters.

Long-term predictions remain accurate for unknown system parameters.

Abstract

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.