Overview

Expertise

Experimental Modeling (Identification)

• Building models from data, not from first principles (physical, chemical, ... laws)
• Improving models from first principles with the help of measurement data (grey-box models)
• Metamodeling: Fast models than approximate complex, time-consuming calculations. Suited for dynamic simulation, optimization, real-time application
• Nonlinear and/or dynamic models

Design of Experiments

• Where to measure?(not location but which operating points?)
• How to describe the operating regime boundaries?

Input Selection

• Which inputs are how relevant for modeling?
• Which inputs have strongly nonlinear effects?

Classification, novelty detection, extrapolation detection, ...

Models: What For?

• Models are the basis for most advanced techniques in many disciplines.
• Model-based techniques can be divided into a (sometimes iterative) two-step procedure:
  1. Building a model.
  2. Using a technique based on this model.
• Applications of models:

 Models: What From?

Topics

1. Experimental Modeling (Identification)

• Local model networks.

2. Design of Experiments (DoE)

• Optimal maximin latin hypercubes (unsupervised).
• Optimization of next measurement point to gather most information with HILOMOT-DoE (supervised).

3. Automatic Input Selection

4. Metamodeling

• For CFD, FEM, and Look-up Tables.

5. Nonlinear Dynamic Models

• Local model networks with OBF and FIR.

6. Classification

• Least squares support vector machine.

7. Design of Excitation Signals for Identification

8. Applications

• Combustion engines
• Driveability calibration
• Automatic transmission modeling
• Structural health monitoring
• Metamodels for CFD simulations
• Metamodels for FEM simulations
• Location estimation for inductive charging