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Bayesian Optimization DoE Python Examples

Getting Started with Bayesian Optimization (BoFire)

A practical walkthrough of using the BoFire Python package to optimize chemical processes efficiently

Blocking in Practice: How to Actually Do It

Learn how blocking is implemented in experimental design through confounding, see a practical comparison of blocked vs. unblocked designs, and understand why randomization alone isn't enough.

Getting Started with Bayesian Optimization (NEXTorch)

A practical walkthrough of using the NEXTorch Python package to optimize chemical processes efficiently

Getting Started with Bayesian Optimization (BayBE)

A practical walkthrough of using the BayBE Python package to optimize chemical processes efficiently

A Technical Guide to Bayesian Optimization

How Bayesian Optimization decides which experiment to run next: surrogate models approximate your expensive experiments, and acquisition functions balance exploring the unknown with exploiting what looks promising.

Why You Should Always Code Your Variables

Learn why coding variables is essential in DoE. Discover how standardizing factor scales prevents misleading coefficients and enables accurate model interpretation.

The Path of Steepest Ascent

Learn how to use the path of steepest ascent to optimize processes systematically. Includes practical examples for linear models with and without interactions.

Response Surface Methodology (RSM)

Learn how Response Surface Methodology uses sequential experiments and polynomial models to navigate the optimization landscape and find better process conditions step by step.

Why Blocking Matters

A real-world example showing what happens when you skip blocking in your DoE. Learn from this epoxy coating experiment how batch-to-batch variability can confound your results—and how blocking prevents it.

Understanding Systematic and Random Errors

Learn to identify, understand, and manage systematic and random errors in experimental design. Discover how these errors impact accuracy and precision, and master the strategies to minimize their effects on your DoE results.