In this section
@article{Olofsson:2018:10.1016/B978-0-444-64241-7.50136-1,
author = {Olofsson, S and Deisenroth, MP and Misener, R},
doi = {10.1016/B978-0-444-64241-7.50136-1},
journal = {Computer Aided Chemical Engineering},
pages = {847--852},
title = {Design of Experiments for Model Discrimination using Gaussian Process Surrogate Models},
url = {http://dx.doi.org/10.1016/B978-0-444-64241-7.50136-1},
volume = {44},
year = {2018}
}
TY - JOUR
AB - © 2018 Elsevier B.V. Given rival mathematical models and an initial experimental data set, optimal design of experiments for model discrimination discards inaccurate models. Model discrimination is fundamentally about finding out how systems work. Not knowing how a particular system works, or having several rivalling models to predict the behaviour of the system, makes controlling and optimising the system more difficult. The most common way to perform model discrimination is by maximising the pairwise squared difference between model predictions, weighted by measurement noise and model uncertainty resulting from uncertainty in the fitted model parameters. The model uncertainty for analytical model functions is computed using gradient information. We develop a novel method where we replace the black-box models with Gaussian process surrogate models. Using the surrogate models, we are able to approximately marginalise out the model parameters, yielding the model uncertainty. Results show the surrogate model method working for model discrimination for classical test instances.
AU - Olofsson,S
AU - Deisenroth,MP
AU - Misener,R
DO - 10.1016/B978-0-444-64241-7.50136-1
EP - 852
PY - 2018///
SN - 1570-7946
SP - 847
TI - Design of Experiments for Model Discrimination using Gaussian Process Surrogate Models
T2 - Computer Aided Chemical Engineering
UR - http://dx.doi.org/10.1016/B978-0-444-64241-7.50136-1
VL - 44
ER -