The objective with this proposal is to provide design tools and algorithms for model management in robust, adaptive and autonomous engineering systems. The increasing demands on reliable models for systems of ever greater complexity have pointed to several insufficiencies in today's techniques for model construction. The proposal addresses key areas where new ideas are required. Modeling a central issue in many scientific fields. System Identification is the term used in the Automatic Control Community for the area of building mathematical models of dynamical systems from observed input and output signals, but several other research communities work with the same problem under different names, such as (data-driven) learning.We have identified five specific themes where progress is both acutely needed and feasible:1. Encounters with Convex Programming Techniques: How to capitalize on the remarkable recent progress in convex and semidefinite programming to obtain efficient, robust and reliable algorithmic solutions.2. Fundamental Limitations: To develop and elucidate what are the limits of model accuracy, regardless of the modeling method. This can be seen as a theory rooted in the Cramer-Rao inequality in the spirit of invariance results and lower bounds characterizing, e.g., Information Theory.3. Experiment Design and Reinforcement Techniques: Study how well tailored and ``cheap'' experiments can extract essential information about isolated model properties. Also study how such methods may relate to general reinforcement techniques.4. Potentials of Non-parametric Models: How to incorporate and adjust techniques from adjacent research communities, e.g. concerning manifold learning and Gaussian Processes in machine learning.5. Managing Structural Constraints: To develop structure preserving identification methods for networked and decentralized systems.We have ideas how to approach each of these themes, and initial attempts are promising.