Optimal experiment design under parametric uncertainty: A comparison of a sensitivities based approach versus a polynomial chaos based stochastic approach
Authors:
- Philippe NimmegeersPhilippe Nimmegeers
- Satyajeet BhonsaleSatyajeet Bhonsale
- Dries TelenDTDries Telen
- Jan Van ImpeJIJan Van Impe
- 1 August 2020
- journal article
- research article
- Published by Elsevier in Chemical Engineering Science
- Vol. 221
Abstract
No abstract availableKeywords
Funding Information
- PFV
- FWO
- BelSPo
- Agency for Innovation through Science and Technology in Flanders (03/2016-03/2020)
This publication has 43 references indexed in Scilit:
- Optimal experiment design under process noise using Riccati differential equationsJournal of Process Control, 2013
- Maximizing the Information Content of Experiments in Systems BiologyPLOS Computational Biology, 2013
- Sampling Decisions in Optimum Experimental Design in the Light of Pontryagin's Maximum PrincipleSIAM Journal on Control and Optimization, 2013
- A backoff strategy for model‐based experiment design under parametric uncertaintyAIChE Journal, 2009
- Integrating parameter selection with experimental design under uncertainty for nonlinear dynamic systemsAIChE Journal, 2008
- On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programmingMathematical Programming, 2005
- The optimal design of dynamic experimentsAIChE Journal, 1989
- Robust experiment design via stochastic approximationMathematical Biosciences, 1985
- Optimum Designs in Regression ProblemsThe Annals of Mathematical Statistics, 1959
- The Homogeneous ChaosAmerican Journal of Mathematics, 1938