Error estimates for model order reduction of Burgers’ equation
نویسندگان
چکیده
منابع مشابه
Optimal order reduction for the the two-dimensional burgers' equation
Two popular model reduction methods, the proper orthogonal decomposition (POD), and balanced truncation, are applied together with Galerkin projection to the twodimensional Burgers’ equation. This scalar equation is chosen because it has a nonlinearity that is similar to the NavierStokes equation, but it can be accurately simulated using far fewer states. However, the number of states required ...
متن کاملOn Optimal Order Error Estimates for the Nonlinear Schrödinger Equation
Implicit Runge–Kutta methods in time are used in conjunction with the Galerkin method in space to generate stable and accurate approximations to solutions of the nonlinear (cubic) Schrödinger equation. The temporal component of the discretization error is shown to decrease at the classical rates in some important special cases.
متن کاملModel order reduction for nonlinear Schrödinger equation
We apply the proper orthogonal decomposition (POD) to the nonlinear Schrödinger (NLS) equation to derive a reduced order model. The NLS equation is discretized in space by finite differences and is solved in time by structure preserving symplectic midpoint rule. A priori error estimates are derived for the POD reduced dynamical system. Numerical results for one and two dimensional NLS equations...
متن کاملApproximate Damped Oscillatory Solutions for Generalized KdV-Burgers Equation and Their Error Estimates
and Applied Analysis 3 25 , the traveling wave solution of 1.2 is a damped oscillatory solution which has a bell profile head. However, they did not present any analytic solution or approximate solution for 1.2 . Xiong 25 obtained a kink profile solitary wave solution for KdV-Burgers equation. In 26 , S. D. Liu and S. D. Liu obtained an approximate damped oscillatory solution to a saddle-focus ...
متن کاملError Estimation for Arnoldi-based Model Order Reduction of MEMS
In this paper we present two different, heuristic error estimates for the Pade-type approximation of transfer functions via an Arnoldi algorithm. We first suggest a convergence criterion between two successive reduced models of the order and . We further propose to use the solution of the Lyapunov equations for reduced-order systems as a stop-criterion during iterative model order reduction.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2020
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2020.12.1575