The use of machine learning and AI in the design of experiments is a rapidly growing area of research and development. There have been several recent advancements in this area, including the development of algorithms for experimental design, optimization, and analysis.

One key advantage of using machine learning and AI in experimental design is the ability to handle large quantities of data and complex models. These techniques can help identify important variables and interactions between them, allowing researchers to optimize their experiments for maximum efficiency and accuracy.

In addition, machine learning and AI can be used to automate certain aspects of experimental design and analysis, reducing the time and resources required for experimentation.

Overall, the state of the art in machine learning and AI for experimental design is rapidly evolving, and there are many exciting new developments on the horizon.

In the design of experiments, machine learning and AI techniques can be used to address a variety of tasks, including:

Overall, the use of machine learning and AI in the design of experiments offers a powerful set of tools for optimizing experimental design, analyzing data, and generating new insights into complex systems.

Dear Jakub Jagielski ,

The recent increase in digitalization of industrial systems has resulted in a boost in data availability in the industrial environment. This has favored the adoption of machine learning (ML) methodologies for the analysis of data, but not all contexts boast data abundance. When data are scarce or costly to collect, Design of Experiments (DOE) can be used to provide an informative dataset for analysis using ML techniques.

Article Design of Experiments and machine learning for product innov...

Regards,

Shafagat