Work

Dynamic flow experiments for Bayesian optimization of a single process objective

Year: 2024

Type: article

Abstract: DynO guides an experimental optimization campaign by suggesting the conditions to use in dynamic flow experiments. DynO is supported by a Gaussian process and stopping criteria, efficiently combining dynamic experiments and Bayesian optimization.

Source: Reaction Chemistry & Engineering

DOI: 10.1039/d4re00543k

Authors: Federico Florit, Kakasaheb Y. Nandiwale, Cameron T. Armstrong, Katharina Grohowalski, Angel R. Diaz, Jason Mustakis, Steven M. Guinness, Klavs F. Jensen

Institutions: Massachusetts Institute of Technology, Pfizer (United States)

Language: English

Topic: Optimal Experimental Design Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Funder: Pfizer

OpenAlex ID: W4405288309

FWCI: 2.473

Cites: 24

Cited by: 8

Related to: 10