Bayesian optimisation; multi-objective optimisation; bone neotissue engineering
Abstract :
[en] We consider optimising bone neotissue growth in a 3D scaffold during dynamic perfusion
bioreactor culture. The goal is to choose design variables by optimising two conflicting
objectives: (i) maximising neotissue growth and (ii) minimising operating cost. Our contribution
is a novel extension of Bayesian multi-objective optimisation to the case of one
black-box (neotissue growth) and one analytical (operating cost) objective function, that
helps determine, within a reasonable amount of time, what design variables best manage
the trade-off between neotissue growth and operating cost. Our method is tested against
and outperforms the most common approach in literature, genetic algorithms, and shows
its important real-world applicability to problems that combine black-box models with
easy-to-quantify objectives like cost.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
olofsson, Simon; Imperial College London > Dept. of Computing
Mehrian, Mohammad ; Université de Liège > Département d'aérospatiale et mécanique > Génie biomécanique
Geris, Liesbet ; Université de Liège > Département d'aérospatiale et mécanique > Génie biomécanique
Calandra, Roberto; University of California, Berkeley > Dept. of EECS,
Deisenrotha; Imperial College London > Dept. of Computing
Ruth, Misener; Imperial College London > Dept. of Computing
Language :
English
Title :
Bayesian Multi-Objective Optimisation of Neotissue Growth in a Perfusion Bioreactor Set-up
Publication date :
01 October 2017
Event name :
Proceedings of the 27th European Symposium on Computer Aided Process Engineering (ESCAPE)
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