Machine Learning Supporting Experimental Design for Product Development in the Lab
| Autor: | Andreas Dinges, Hergen Schultze, Horst Weiss, Gregor Foltin, Michael Bortz, Jens Babutzka, David Hajnal |
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| Přispěvatelé: | Publica |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Estimation theory business.industry General Chemical Engineering Model selection 010401 analytical chemistry General Chemistry Machine learning computer.software_genre 01 natural sciences Industrial and Manufacturing Engineering 0104 chemical sciences 010104 statistics & probability New product development Artificial intelligence 0101 mathematics business computer |
| Popis: | An interactive decision support framework is presented that assists lab researchers in finding optimal product recipes. Within this framework, an approach for sequential experimental design for black box models in a multicriteria optimization context is introduced. An additional criterion involving the prediction error to design new experiments is used with the goal to get a reliable estimate of the Pareto frontier within a few experimental iterations. The resulting decision support approach accompanies the chemist through the whole workflow and supports the user via interactive, graphical elements. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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