To explore what the future of multidimensional experiments might look like, we decided to look back.
In this episode, we explored how different multi-dimensional (aka Design of Experiments, or DOE) methods have come about to date.
Then, we pondered how these different methods, together with tech and software innovations, have brought both opportunities to scale multidimensional experimentation, and exciting questions on the best way to go about it.
Conversation highlights
00:00 - Introduction
01:04 - The question mark around automation: what to do with 1000+ runs
10:15 - History of multidimensional (Design of Experiments / DOE) methods to date:
10:58 - Era 1: Factorial designs came about, and benefited agriculture
15:12 - Era 2: Response surface methods emerged in process industries
18:17 - Era 3: Software packages helped design the “optimal” experiment
21:29 - “DOE 4.0”: Bayesian optimization, and the pros and cons for biology