Utilising Machine Learning to help better inform your next DOE
Date: 18 March 2021
Time: 10:30 - 11:00 GMT | 11:30 - 12:00 CET
When designing experiments do you wonder if you have identified all of the potentially important factors? Do you have concerns that the range over which you choose to vary your factors may subsequently need revising? Have you used statistical analysis of existing data with the goal of better informing your DOE choices but find the data resulting from your DOE did not answer your questions?
Please spend 30 minutes with us exploring what is possible with machine learning to better inform your next set of experiments and reduce the potential for errors. Invite your colleagues along so you can explore together what is possible. You will gain an understanding of:
- How machine learning can help you analyse existing data to determine the full set of factors that might be influencing your outcomes.
- How machine learning avoids the risk of over-looking one or more potentially important factors.
- How by integrating machine learning with DOE you work smarter to achieve project outcomes predictably and in less time, reducing stress.
Register now to attend online.
ABOUT THE SPEAKER
Christian Ramskov Larsen is a Systems Engineer for JMP. With a background in materials and manufacturing engineering, Larsen previously worked as a consulting analyst for pharmaceutical engineering consulting firm NNE. Larsen holds a Master of Science and Bachelor of Science in engineering, both from Danmarks Tekniske Universitet.