output
20042026
most citedComputing knock out strategies in metabolic networks

81 citations

Showing 2021Show all

5 papers · 1 filter

cs.LG20216 cited

Learning Dynamics from Noisy Measurements using Deep Learning with a Runge-Kutta Constraint

Pawan Goyal, Peter Benner

Measurement noise is an integral part while collecting data of a physical process. Thus, noise removal is a necessary step to draw conclusions from these data, and it often becomes…

math.NA20212 cited

Learning reduced order models from data for hyperbolic PDEs

Neeraj Sarna, Peter Benner

Given a set of solution snapshots of a hyperbolic PDE, we are interested in learning a reduced order model (ROM). To this end, we propose a novel decompose then learn approach. We…

eess.SY20211 cited

Data-driven modeling and control of large-scale dynamical systems in the Loewner framework

Ion Victor Gosea, Charles Poussot-Vassal, Athanasios C. Antoulas

In this contribution, we discuss the modeling and model reduction framework known as the Loewner framework. This is a data-driven approach, applicable to large-scale systems, which…

cs.LG20215 cited

LQResNet: A Deep Neural Network Architecture for Learning Dynamic Processes

Pawan Goyal, Peter Benner

Mathematical modeling is an essential step, for example, to analyze the transient behavior of a dynamical process and to perform engineering studies such as optimization and contro…

math.NA20212 cited

Factorization of the Loewner matrix pencil and its consequences

Qiang Zhang, Ion Victor Gosea, Athanasios C. Antoulas

This paper starts by deriving a factorization of the Loewner matrix pencil that appears in the data-driven modeling approach known as the Loewner framework and explores its consequ…