Model Order Reduction: Theory, Research Aspects and Applications

By K. Fujimoto, J. Scherpen. 2008

573 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Numerical Analysis, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/393f9bbd70bf2d5df58ea69312b0380304d9381d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Angela P. Schoellig
2. Martin Gubisch
3. Stefan Volkwein
4. Christian Gogu

In Numerical Analysis

1. Martin Gubisch
2. Stefan Volkwein
3. Christian Gogu

In Statistical and Nonlinear Physics

1. Martin Gubisch
2. Stefan Volkwein
3. Christian Gogu
4. René Pinnau
5. Karen E. Willcox
6. Anthony T. Patera
7. Anthony Nouy
8. Alessandro Astolfi

Citations in the corpus

• Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning (2021, methodology): “In (114), an ROA estimation approach for high-dimensional systems is presented, combining a sum of squares (SOS) programming method for the ROA computation (115) and a dynamics model order reduction technique to curtail computational complexity (116).”
• Proper Orthogonal Decomposition for Linear-Quadratic Optimal Control (2013, methodology): “Proper orthogonal decomposition, model-order reduction, apriori and a-posteriori error analysis, linear-quadratic optimal control, primal-dual active set strategy.”
• Computational Methods for Linear Matrix Equations (2016, unknown): “, [4],[258],[91],[70],[23],[28],[224],[63] and references therein.”
• Improving the efficiency of large scale topology optimization through on‐the‐fly reduced order model construction (2015, background): “Model order reduction [22] is a family of approaches that aims at significantly decreasing the computational burden associated with the inversion of the system in Equation (3).”