Response surface methodology

By A. I. Khuri, Siuli Mukhopadhyay. 2010

3,894 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Environmental Chemistry, Management Science and Operations Research, Statistics and Probability, Mathematical Optimization

How researchers see this paper

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

In Computer Networks and Communications

In Environmental Chemistry

In Management Science and Operations Research

In Statistics and Probability

In Mathematical Optimization

Citations in the corpus

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information (2018, background): “in principle available to solve (1) and (2) include response surface methods (internal) (Kleijnen, 2015), stochastic search (internal) (Andradóttir, 2015, Hu, 2015, Zabinsky, 2015) and versions of stochastic approximation (internal) where knowledge of (2) is limited to the ability to perform evaluat”
Response surface design evaluation and comparison (2009, neutral): “Myers and Montgomery (2002) suggest that a good design should: 1.”
Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures (2020, methodology): “Traditionally, regression and response surface methodology (RSM) are widely used for metamodeling [15–17] which are based on the polynomial lease-square ﬁtting.”
A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design (2016, methodology): “The response surface method (RSM) is a collection of mathematical techniques based on the fit of a polynomial equation to the experimental data obtained in relation to the experimental design [22].”
Reliability-Based Design Optimization of Nonlinear Aeroelasticity Problems (2010, background): “Subcritical LCOs require a special treatment in aeroelastic design, not only because they appear before the critical flutter velocity, but also because they lead to discontinuous responses that hamper the use of classical computational design tools for optimization or reliability assessment.”
Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping (2008, methodology): “These models can be roughly divided into three categories: data-fit surrogates such as response surfaces [1,2]; kriging [3]; radial basis functions [4] or extended radial basis functions [5,6]; reduced-order models, derived using techniques such as proper orthogonal decomposition [7] and modal analy”
Novel adsorbent from agricultural waste (cashew NUT shell) for methylene blue dye removal: Optimization by response surface methodology (2015, methodology): “The main objective of RSM is to determine the optimum operational conditions for the system or to determine a region that satisfies the operating specifications [68].”
Breakthrough adsorption study of a commercial activated carbon for pre-combustion CO2 capture (2011, positive): “This methodology involves the design of experiments and multiple regression analysis as tools to assess the effects of two or more independent variables on dependent variables [23].”
Optimum stacking sequence design of composite materials Part II: Variable stiffness design (2010, methodology): “The response surface method [69] is another means of generating an approximate model of a computationally extensive function.”