# Data-Driven System Reduction and Identification

Nowadays, many processes in engineering and natural sciences can be modeled, studied and optimized based on large collections of measured data. The goal is to use the available resources to devise reliable and efficient data-driven reduction methods.

- Prof. Antoulas is renowned for his fundamental research contributions
- This Festschrift is being published for a conference to celebrate Antoulas's 70th birthday
- Model reduction is a timely and important area with many scientific and engineering applications.

2. A posteriori error bounds in reduced-order modeling for which the main tool is the use of the dual system.

3. Addressing issues such as transfer functions of rectangular systems, stability, and DAE structure preservation.

4. Research toward discovering how worms implement control mechanism. For access to the presentation press: more