scholarly journals Multimodal information gain in Bayesian design of experiments

2021 ◽  
Author(s):  
Quan Long
Keyword(s):  
Design Of Experiments ◽  
Information Gain ◽  
Bayesian Design ◽  
Multimodal Information

Decision Oriented Bayesian Design of Experiments

2009 ◽  
Vol 42 (11) ◽  
pp. 655-660
Author(s):  
Farminder S. Anand ◽  
Jay H. Lee ◽  
Matthew J. Realff
Keyword(s):  
Design Of Experiments ◽  
Bayesian Design

scholarly journals Experimental Design in Polymer Chemistry—A Guide towards True Optimization of a RAFT Polymerization Using Design of Experiments (DoE)

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3147
Author(s):  
Tilman Eckert ◽  
Florian C. Klein ◽  
Piet Frieler ◽  
Oliver Thunich ◽  
Volker Abetz
Keyword(s):  
Design Of Experiments ◽  
Prediction Models ◽  
Information Gain ◽  
Raft Polymerization ◽  
Academic Research ◽  
Monomer Conversion ◽  
Individual Response ◽  
Molecular Weights ◽  
Reversible Addition ◽  
Face Centered

Despite the great potential of design of experiments (DoE) for efficiency and plannability in academic research, it remains a method predominantly used in industrial processes. From our perspective though, DoE additionally provides greater information gain than conventional experimentation approaches, even for more complex systems such as chemical reactions. Hence, this work presents a comprehensive DoE investigation on thermally initiated reversible addition–fragmentation chain transfer (RAFT) polymerization of methacrylamide (MAAm). To facilitate the adaptation of DoE for virtually every other polymerization, this work provides a step-by-step application guide emphasizing the biggest challenges along the way. Optimization of the RAFT system was achieved via response surface methodology utilizing a face-centered central composite design (FC-CCD). Highly accurate prediction models for the responses of monomer conversion, theoretical and apparent number averaged molecular weights, and dispersity are presented. The obtained equations not only facilitate thorough understanding of the observed system but also allow selection of synthetic targets for each individual response by prediction of the respective optimal factor settings. This work successfully demonstrates the great capability of DoE in academic research and aims to encourage fellow scientists to incorporate the technique into their repertoire of experimental strategies.

Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Piyush Pandita ◽  
Ilias Bilionis ◽  
Jitesh Panchal
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Information Gain ◽  
Black Box ◽  
Expected Information ◽  
Bayesian Optimal Design ◽  
Physical Response ◽  
Hypothetical Experiment ◽  
Statistical Expectation ◽  
Expected Information Gain

Abstract Bayesian optimal design of experiments (BODEs) have been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is characterized by sequentially querying the function at specific designs selected by an infill-sampling criterion. However, most current BODE methods operate in specific contexts like optimization, or learning a universal representation of the black-box function. The objective of this paper is to design a BODE for estimating the statistical expectation of a physical response surface. This QoI is omnipresent in uncertainty propagation and design under uncertainty problems. Our hypothesis is that an optimal BODE should be maximizing the expected information gain in the QoI. We represent the information gain from a hypothetical experiment as the Kullback–Liebler (KL) divergence between the prior and the posterior probability distributions of the QoI. The prior distribution of the QoI is conditioned on the observed data, and the posterior distribution of the QoI is conditioned on the observed data and a hypothetical experiment. The main contribution of this paper is the derivation of a semi-analytic mathematical formula for the expected information gain about the statistical expectation of a physical response. The developed BODE is validated on synthetic functions with varying number of input-dimensions. We demonstrate the performance of the methodology on a steel wire manufacturing problem.

scholarly journals Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process

2020 ◽  
Vol 262 ◽  
pp. 114533 ◽  
Author(s):  
Joshua C. Morgan ◽  
Anderson Soares Chinen ◽  
Christine Anderson-Cook ◽  
Charles Tong ◽  
John Carroll ◽  
...  
Keyword(s):  
Design Of Experiments ◽  
Co2 Capture ◽  
Pilot Scale ◽  
Bayesian Design ◽  
Capture Process

Diagnostic Checks and Measures of Information in the Bayesian Design of Experiments with Complex Polymerizations

2011 ◽  
Vol 302 (1) ◽  
pp. 90-99 ◽  
Author(s):  
Afsaneh Nabifar ◽  
Neil T. McManus ◽  
Eduardo Vivaldo-Lima ◽  
Alexander Penlidis
Keyword(s):  
Design Of Experiments ◽  
Bayesian Design
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