Robust data-driven coarse-graining for surrogate modeling (B03)

• Subject Area: Mathematics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 442047500

Determining effective low-dimensional reduced order models from observations of a system with widely separated scales can, however, be severely ill-posed. In this project, we will develop and analyze novel parametric as well as non-parametric methodologies with provablestability and robustness guarantees. The mathematical foundation of these approaches will be based on connecting techniques of the theory of homogenization for singularly perturbed stochastic dynamical systems, regularization techniques for inverse problems, and Bayesian learning methodologies.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1481: Sparsity and Singular Structures
• Applicant Institution: Rheinisch-Westfälische Technische Hochschule Aachen
• Project Head: Professor Dr. Sebastian Krumscheid