activity
20192021
most citedHyperbolic Discretization via Riemann Invariants

3 citations · 8 across the 5 of their papers we have counts for

collaborators

9 papers

math.OC2021

Next-Gen Gas Network Simulation

Christian Himpe, Sara Grundel, Peter Benner

To overcome many-query optimization, control, or uncertainty quantification work loads in reliable gas and energy network operations, model order reduction is the mathematical tech…

math.NA2021

Model-Order Reduction For Hyperbolic Relaxation Systems

Sara Grundel, Michael Herty

We propose a novel framework for model-order reduction of hyperbolic differential equations. The approach combines a relaxation formulation of the hyperbolic equations with a discr…

math.OC20203 cited

How much testing and social distancing is required to control COVID-19? Some insight based on an age-differentiated compartmental model

Sara Grundel, Stefan Heyder, Thomas Hotz +3

In this paper, we provide insights on how much testing and social distancing is required to control COVID-19. To this end, we develop a compartmental model that accounts for key as…

math.OC2020

Model Order Reduction for Gas and Energy Networks

Christian Himpe, Sara Grundel, Peter Benner

To counter the volatile nature of renewable energy sources, gas networks take a vital role. But, to ensure fulfillment of contracts under these circumstances, a vast number of poss…

eess.SY20201 cited

Nonlinear model reduction of dynamical power grid models using quadratization and balanced truncation

Tobias K. S. Ritschel, Frances Weiß, Manuel Baumann +1

In this work, we present a nonlinear model reduction approach for reducing two commonly used nonlinear dynamical models of power grids: the effective network (EN) model and the syn…

math.NA20203 cited

Hyperbolic Discretization via Riemann Invariants

Sara Grundel, Michael Herty

We are interested in numerical schemes for the simulation of large scale gas networks. Typical models are based on the isentropic Euler equations with realistic gas constant. The n…