Optimization based model order reduction for stochastic systems
作者：
Highlights：
• New error bound analysis for model order reduction methods applied to stochastic systems with additive and multiplicative noise.
• Derivation of optimality conditions for these error bounds guaranteeing local minimality of these bounds.
• Provision of algorithms that lead to reduced order models satisfying the optimality conditions.
• Numerical experiments that show the effectiveness of the reduced systems.
摘要
•New error bound analysis for model order reduction methods applied to stochastic systems with additive and multiplicative noise.•Derivation of optimality conditions for these error bounds guaranteeing local minimality of these bounds.•Provision of algorithms that lead to reduced order models satisfying the optimality conditions.•Numerical experiments that show the effectiveness of the reduced systems.
论文关键词：Model order reduction,Stochastic systems,Optimality conditions,Sylvester equations,Lévy process
论文评审过程：Received 4 August 2020, Revised 24 October 2020, Accepted 1 November 2020, Available online 30 January 2021, Version of Record 30 January 2021.
论文官网地址：https://doi.org/10.1016/j.amc.2020.125783
