---
title: DoE & Bayesian Optimization Dashboard
emoji: 🔬
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: "6.13.0"
app_file: app.py
pinned: true
license: mit
short_description: Design experiments & get AI-guided suggestions
tags:
  - bayesian-optimization
  - design-of-experiments
  - gaussian-process
  - machine-learning
  - scientific-computing
---

# 🔬 Design of Experiments & Bayesian Optimization Dashboard

A comprehensive tool for experimentalists to plan experiments, fit surrogate models, and get AI-guided suggestions for the next most informative experiments.

## Features

### 📐 Design of Experiments
- **Full Factorial** — All combinations of factor levels
- **Fractional Factorial (½, ¼)** — Reduced experiment count for screening
- **Latin Hypercube Sampling (LHS)** — Space-filling design for BO initialization
- **Sobol Sequence** — Low-discrepancy quasi-random design
- **Central Composite Design (CCD)** — Response surface methodology
- **Box-Behnken** — Efficient 3+ factor designs
- Flexible factor definition: comma-separated levels or range notation (`10:30:3`)
- Add/remove rows and columns dynamically
- CSV export

### 🤖 Surrogate Models
- **Gaussian Process** with 5 kernel choices (Matérn 5/2, Matérn 3/2, RBF, Rational Quadratic, RBF+Matérn)
- **Random Forest** with tree-ensemble uncertainty
- **Extra Trees** — faster RF variant
- **Gradient Boosting** with staged prediction uncertainty

### 🎯 Acquisition Functions
- **Expected Improvement (EI)** — balanced exploration/exploitation
- **Probability of Improvement (PI)** — greedy exploitation
- **Upper Confidence Bound (UCB)** — tunable exploration weight
- **Thompson Sampling** — stochastic batch selection
- Diverse suggestion selection (avoids clustering)

### 📊 12 Visualization Types (all toggleable)
1. 🗺️ Surrogate Surface (2D contour with mean + uncertainty)
2. 📉 1D Parameter Slices (effect of each factor)
3. 🏔️ 3D Response Surface (interactive Plotly)
4. 🎯 Acquisition Function Landscape
5. 📈 Convergence Plot (best-so-far trajectory)
6. ⚖️ Predicted vs Actual (parity plot with R²/RMSE)
7. 📊 Feature Importance (GP lengthscales / RF importances)
8. 📏 Residual Analysis (residuals, histogram, Q-Q plot)
9. 🔀 Parallel Coordinates (interactive Plotly)
10. 🌡️ Uncertainty Heatmap
11. 🔗 Correlation Matrix
12. 🔘 Scatter Matrix (pair plot)

## Workflow

1. **Define Factors** → Set experimental parameters and levels
2. **Generate Design** → Choose a design type (factorial, LHS, CCD, etc.)
3. **Run Experiments** → Fill in the Response column with results
4. **Fit Surrogate** → Configure model (GP/RF/ET/GB) and acquisition function
5. **Get Suggestions** → Receive optimized next-experiment recommendations
6. **Iterate** → Add suggestions to table, run them, repeat

## Tech Stack
- Gradio (UI), scikit-learn (GP, RF, ET, GB), scikit-optimize, scipy (LHS, Sobol, acquisition optimization), Plotly + Matplotlib (visualizations)