2. Design of Experiments (DoE)
Task
- Where to measure in order to optimally improve a model?
 
Difficulty with Traditional Approaches
- Unsupervised → Good for initialization.
 - Whole measurement plan is designed a priori.
 - Model quality is either
- not considered at all (space filling designs, etc.) or
- only variance component is optimized (D-optimal, etc.). 
Our Approach
- Active learning → Feedback of model quality to DoE.
 - Focus rather on dominating bias (systematic) error than on variance (stochastic) error.
 - Utilizes powerful local network construction algorithms → HILOMOT.
 - Incremental procedure → Measurement can be stopped anytime.
 
Optimal maximin latin hypercubes (unsupervised)
Optimization of Latin Hypercube (LH) Designs
- LH not necessarily space filling → Optimization required. Here: maximin or Φp.
 - Goal: Maximization of the critical points (closest to each other).
 - Implementation: Deterministic optimization strategy.
 
Optimization Strategy (Phase 1)
- Initialize with random LH
 - Find critical points with minimal distance to each other
 - Go through all critical points (red)
 - Try to swap with other points (brown) in order to increase critical distance
 - Check all potential swapping partners in all dimensions for all critical points
 - Iterate until no improving partner can be found anymore
 - Subsequent phase 2 improves further
 
Example: 5 points / 2 dimensions
 
LH Optimization for 100 Points (Phase 1: Improving Critical Points)
      
    
LH Optimization for 100 Points (Phase 2: Further Improvements)
      
    
Optimization of next measurement point to gather most information (supervised)
HILOMOT-DoE: Active Learning Cycle
      
    
Joint project with 
    
 
Situation
- Increasing degrees of freedom.
 - More potential for optimization.
 
Problem
- Cost and engine test stand times overwhelming.
 - Individual approaches become tedious.
 - Advanced systematic procedures required.
 
Results
- Sophisticated models intertwined with DoE (active learning).
 - Much faster engine measurement/calibration times.
 - Flexible trade-off between model accuracy and required engine test stand times.
 
