A variance reduction method based on sensitivity derivatives

DOI10.1016/j.apnum.2005.06.010zbMath1098.65003OpenAlexW1983054583MaRDI QIDQ2495434

Yanyan Li

Publication date: 30 June 2006

Published in: Applied Numerical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apnum.2005.06.010

zbMATH Keywords

numerical experimentsstochastic partial differential equationsMonte Carlo methodBurgers' equationvariance reduction

Mathematics Subject Classification ID

Monte Carlo methods (65C05) KdV equations (Korteweg-de Vries equations) (35Q53) Stochastic partial differential equations (aspects of stochastic analysis) (60H15) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (4)

Using the Monte Carlo method to solve integral equations using a modified control variate ⋮ Uncertainty quantification in a chemical system using error estimate-based mesh adaption ⋮ Variance reduction method based on sensitivity derivatives. II. ⋮ A systematic study of efficient sampling methods to quantify uncertainty in crack propagation and the Burgers equation

Uses Software

• CFL3D

Cites Work

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• First- and second-order aerodynamic sensitivity derivatives via automatic differentiation with incremental iterative methods
• An efficient Monte Carlo method for optimal control problems with uncertainty
• Stochastic approaches to uncertainty quantification in CFD simulations
• Variance Reduction Techniques for Estimating Value-at-Risk
• Uncertainty Quantification in CFD Simulations: A Stochastic Spectral Approach

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