Empirical Differential Gramians for Nonlinear Model Reduction
For engineers working on nonlinear model reduction, this provides a practical alternative to conventional balancing methods that require solving PDEs.
The paper presents an empirical balanced truncation method for nonlinear systems with linear input vector fields, enabling model reduction without solving nonlinear PDEs. The method is demonstrated on an RL network trajectory.
In this paper, we present an empirical balanced truncation method for nonlinear systems with linear time-invariant input vector field components. First, we define differential reachability and observability Gramians. They are matrix valued functions of the state trajectory (i.e. the initial state and input trajectory) of the original nonlinear system, and it is difficult to find them as functions of the initial state and input. The main result of this paper is to show that for a fixed state trajectory, it is possible to compute the values of these Gramians by using impulse and initial state responses of the variational system. Therefore, balanced truncation is doable along the fixed state trajectory without solving nonlinear partial differential equations, differently from conventional nonlinear balancing methods. We further develop an approximation method, which only requires trajectories of the original nonlinear systems. Our methods are demonstrated by an RL network along a trajectory.