The right tool for the right job – ML and Design of Experiments
Typical statistical DOE software assumes that the response of experimental outputs to inputs is linear, or at best quadratic. ML makes no such assumption. Its models learn from the data provided even when that data contains complex, non-linear relationships. So ML can model difficult multi-component systems where cross-correlations would not be accounted for by other DOE approaches.
Standard DOE methods usually require you to vary only a limited number of inputs at any one time in your experimental design. With ML, you don’t have to identify which inputs are most important (thus potentially building bias into your design). You can ask the ML to explore all of the inputs simultaneously and it will find those that are most significant.