Empirical model reduction of controlled nonlinear systems

Nonlinear Model Reduction via Discrete Empirical Interpolation

Nonlinear Model Reduction via Discrete Empirical Interpolation by Saifon Chaturantabut This thesis proposes a model reduction technique for nonlinear dynamical systems based upon combining Proper Orthogonal Decomposition (POD) and a new method, called the Discrete Empirical Interpolation Method (DEIM). The popular method of Galerkin projection with POD basis reduces dimension in the sense that ...

Model Order Reduction of Nonlinear Dynamical Systems

Model Order Reduction of Nonlinear Dynamical Systems by Chenjie Gu Doctor of Philosophy in Electrical Engineering and Computer Science University of California, Berkeley Professor Jaijeet Roychowdhury, Chair Higher-level representations (macromodels, reduced-order models) abstract away unnecessary implementation details and model only important system properties such as functionality. This meth...

Model Reduction of Weakly Nonlinear Systems

1. INTRODUCTION As the level of detail and intricacy of dynamical system models continues to grow, the essential behaviour of such systems can be lost in a cloud of complexity. Furthermore, numerical simulations of such systems can consume extensive memory resources and take inordinate amounts of time. Consequently, to cope with the growing complexity and dimension-ality of systems, model reduc...

Attractor Modeling and Empirical Nonlinear Model Reduction of dissipative Dynamical Systems

In a broad sense, model reduction means producing a low-dimensional dynamical system that replicates either approximately, or more strictly, exactly and topologically, the output of a dynamical system. Model reduction has an important role in the study of dynamical systems and also with engineering problems. In many cases, there exists a good low-dimensional model for even very high-dimensional...

Model reduction for nonlinear control systems