adaptive doe

**Adaptive DOE** is a **design of experiments approach that dynamically modifies the experimental plan based on incoming results** — using algorithms (Bayesian optimization, reinforcement learning) to select each next experiment to maximize information gain or expected improvement. **How Adaptive DOE Works** - **Initial Points**: Start with a small space-filling design (Latin Hypercube, random). - **Surrogate Model**: Fit a model (Gaussian process, random forest) to current data. - **Acquisition Function**: Select the next experiment to maximize Expected Improvement, Knowledge Gradient, or other criteria. - **Iterate**: Run the experiment, update the model, select the next point. Repeat until convergence. **Why It Matters** - **Efficiency**: Converges to the optimum in 2-5× fewer experiments than classical DOE. - **Expensive Experiments**: Ideal when each experiment is costly (real wafers, long process times). - **Non-Standard**: Can handle constraints, noisy responses, and multi-fidelity evaluations. **Adaptive DOE** is **experiments guided by AI** — using models to choose the most valuable next experiment in real time.