Many engineering and scientific problems involve finding the best operating conditions while minimizing the number of costly or time-consuming experiments. This session demonstrates how Bayesian Optimization in JMP Pro intelligently guides experimentation by learning from previous results and recommending the next most informative experimental conditions. Using an intuitive plant growth optimization example, we illustrate how Bayesian Optimization balances exploration and exploitation to efficiently search for optimal conditions without exhaustively testing every possible combination. This practical workflow highlights how JMP Pro helps reduce experimental effort, accelerate discovery, conserve valuable resources, and make confident, data-driven decisions across engineering, manufacturing, agriculture, product development, and scientific research.
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