Data-based model order reduction for stochastic dynamics (A07)

• Subject Area: Mathematics
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 318763901

This project develops data-driven model order reduction methods for time-dependent largescale stochastic dynamical systems, where full simulations are often too costly. The goal is to identify low-dimensional structures from potentially noisy data and construct efficient reduced models that approximate the full system. We will apply these methods in a pharmacological context, where variability in treatment response is modelled by complex stochastic systems, but full model estimation is computationally infeasible.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1294: Data Assimilation – The Seamless Integration of Data and Models
• Applicant Institution: Universität Potsdam
• Project Heads: Professorin Dr. Melina Freitag, since 7/2021; Dr. Niklas Hartung; Professor Dr. Han Cheng Lie, from 7/2021 until 12/2025; Professor Dr. Martin Redmann