22 citations · 67 across the 36 of their papers we have counts for
7 papers · 1 filter
Empirical sparse regression on quadratic manifolds
Paul Schwerdtner, Serkan Gugercin, Benjamin Peherstorfer
Approximating field variables and data vectors from sparse samples is a key challenge in computational science. Widely used methods such as gappy proper orthogonal decomposition an…
A non-intrusive data-based reformulation of a hybrid projection-based model reduction method
Ion Victor Gosea, Serkan Gugercin, Christopher Beattie
We present a novel data-driven reformulation of the iterative SVD-rational Krylov algorithm (ISRK), in its original formulation a Petrov-Galerkin (two-sided) projection-based itera…
Time-Domain Iterative Rational Krylov Method
Michael S. Ackermann, Serkan Gugercin
The Realization Independent Iterative Rational Krylov Algorithm (TF-IRKA) is a frequency-based data-driven reduced order modeling (DDROM) method that constructs opti…
Balanced truncation with conformal maps
Alessandro Borghi, Tobias Breiten, Serkan Gugercin
We consider the problem of constructing reduced models for large scale systems with poles in general domains in the complex plane (as opposed to, e.g., the open left-half plane or…
optimal model reduction of linear systems with multiple quadratic outputs
Sean Reiter, Igor Pontes Duff, Ion Victor Gosea +1
In this work, we consider the optimal model reduction of dynamical systems that are linear in the state equation and up to quadratic nonlinearity in the output equation. As o…
Frequency-Based Reduced Models from Purely Time-Domain Data via Data Informativity
Michael S. Ackermann, Serkan Gugercin
Frequency-based methods have been successfully employed in creating high fidelity data-driven reduced order models (DDROMs) for linear dynamical systems. These methods require acce…