README.md

metadata

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)