Machine learning for adaptive experimental design
Machine learning for adaptive experimental design
8 PagesThis white paper by Intellegens presents how machine learning can significantly improve experimental design through an adaptive approach. Traditional Design of Experiments (DOE) methods are limited in handling complex, nonlinear systems and require extensive testing. The Alchemite™ platform enables efficient exploration by learning from sparse, noisy data and iteratively guiding experiments toward optimal outcomes. Case studies from Johnson Matthey, AMRC, and Domino Printing Sciences show reductions of 50–80% in testing workload. This adaptive method accelerates R&D, improves accuracy, and reduces costs without needing deep statistical expertise.
