Surrogate Model Selection for Design Space Approximation And Surrogatebased Optimization
- Auburn Univ., AL (United States); Auburn University
- Auburn Univ., AL (United States)
Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate for sensitivity analysis, uncertainty propagation and surrogate based optimization. This work evaluates the performance of eight surrogate modeling techniques for design space approximation and surrogate based optimization applications over a set of generated datasets with known characteristics. With this work, we aim to provide general rules for selecting an appropriate surrogate model form solely based on the characteristics of the data being modeled. Furthermore, the computational experiments revealed that, in general, multivariate adaptive regression spline models (MARS) and single hidden layer feed forward neural networks (ANN) yielded the most accurate predictions over the design space while Random Forest (RF) models most reliably identified the locations of the optimums when used for surrogate-based optimization.
- Research Organization:
- RAPID Manufacturing Institute (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office
- Grant/Contract Number:
- EE0007888
- OSTI ID:
- 1647743
- Journal Information:
- Computer Aided Chemical Engineering, Journal Name: Computer Aided Chemical Engineering Vol. 47; ISSN 1570-7946
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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