Data-driven methods for partial differential equations

Session

Invited talks 3

Mar 3, 2026, 8:30 AM

Karlsruhe Institute of Technology

Conveners

Invited talks 3: Model order reduction (Speaker: Glas, Peherstorfer)

• Benjamin Unger (Karlsruhe Institute of Technology)

20. Structure-preserving model reduction: From the formulation on manifolds to data-driven realizations

Silke Glas (University of Twente)

3/3/26, 8:30 AM

Invited talk

Capturing and preserving physical properties, e.g., system energy, stability, and passivity, using data-driven methods is currently a highly researched topic in surrogate modeling. To ensure that the desired physical properties are retained, structure-preserving projection techniques are used in model order reduction (MOR).
In this talk, we present structure-preserving MOR with nonlinear...

12. Data-driven reduced modeling of chaotic, turbulent, and stochastic systems via population dynamics

Benjamin Peherstorfer (Courant Institute of Mathematical Sciences, New York University)

3/3/26, 9:15 AM

Invited talk

Learning models of time-dependent processes that generalize across initial conditions and parameter regimes is a key challenge in machine learning and the computational sciences. For chaotic, turbulent, and stochastic systems, modeling the dynamics of individual trajectories can be exceedingly challenging because trajectories can be erratic and irregular, and in stochastic settings may even be...