Full state approximation by Galerkin projection reduced order models for stochastic and bilinear systems
DOI10.1016/j.amc.2021.126561OpenAlexW3210577752MaRDI QIDQ2668350
Martin Redmann, Igor Pontes Duff
Publication date: 3 March 2022
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2102.07534
stability analysiserror boundsmodel order reductionGalerkin projectionstochastic and bilinear systems
Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Nonlinear systems in control theory (93C10) Asymptotic stability in control theory (93D20) Stochastic stability in control theory (93E15) Numerical solutions to stochastic differential and integral equations (65C30) Large-scale systems (93A15)
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Cites Work
- Unnamed Item
- Positive operators and stable truncation
- Stability preservation in projection-based model order reduction of large scale systems
- Stochastic stability of differential equations. With contributions by G. N. Milstein and M. B. Nevelson
- Krylov subspace methods for model order reduction of bilinear control systems
- Model reduction for stochastic systems
- Structure preserving model reduction of port-Hamiltonian systems by moment matching at infinity
- Rational matrix equations in stochastic control.
- Optimization based model order reduction for stochastic systems
- Stability preservation in Galerkin-type projection-based model order reduction
- An \(\mathcal{H}_2\)-type error bound for balancing-related model order reduction of linear systems with Lévy noise
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
- Numerical solution of large and sparse continuous time algebraic matrix Riccati and Lyapunov equations: a state of the art survey
- Dual Pairs of Generalized Lyapunov Inequalities and Balanced Truncation of Stochastic Linear Systems
- Lyapunov Equations, Energy Functionals, and Model Order Reduction of Bilinear and Stochastic Systems
- Computational Methods for Linear Matrix Equations
- Principal component analysis in linear systems: Controllability, observability, and model reduction
- Model reduction via balanced state space representations
- Model reduction methods based on Krylov subspaces
- Type II Singular Perturbation Approximation for Linear Systems with Lévy Noise
- Nonlinear systems – algebraic gramians and model reduction
- Bilinear Systems---A New Link to $\mathcal H_2$-norms, Relations to Stochastic Systems, and Further Properties
- On a Matrix Riccati Equation of Stochastic Control
- Galerkin proper orthogonal decomposition methods for parabolic problems
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