Reduced-Order Modeling and ROM-Based Optimization of Batch Chromatography

DOI10.1007/978-3-319-10705-9_42zbMath1328.65220OpenAlexW126123994MaRDI QIDQ5264868

Yongjin Zhang, Suzhou Li, Peter Benner, Li-Hong Feng

Publication date: 28 July 2015

Published in: Lecture Notes in Computational Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-10705-9_42

Mathematics Subject Classification ID

Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99)

Related Items (5)

An Efficient Output Error Estimation for Model Order Reduction of Parametrized Evolution Equations ⋮ A data-driven surrogate modeling approach for time-dependent incompressible Navier-Stokes equations with dynamic mode decomposition and manifold interpolation ⋮ Model reduction for the material point method via an implicit neural representation of the deformation map ⋮ Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem ⋮ A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

Uses Software

• rbMIT

Cites Work

• An `empirical interpolation' method: Application to efficient reduced-basis discretization of partial differential equations
• Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation
• A posteriorierror bounds for reduced-basis approximations of parametrized parabolic partial differential equations
• Anhpcertified reduced basis method for parametrized parabolic partial differential equations
• A training set and multiple bases generation approach for parameterized model reduction based on adaptive grids in parameter space
• Reduced basis method for finite volume approximations of parametrized linear evolution equations

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