Learning Reduced-Order Quadratic-Linear Models in Process Engineering using 
Operator Inference

Gosea, Ion Victor; Peterson, Luisa; Goyal, Pawan Kumar; Bremer, Jens; Sundmacher, Kai; Benner, Peter

Local TagsRelease HistoryDetailsSummary

Learning Reduced-Order Quadratic-Linear Models in Process Engineering using Operator Inference

Gosea, I. V., Peterson, L., Goyal, P. K., Bremer, J., Sundmacher, K., & Benner, P. (in preparation). Learning Reduced-Order Quadratic-Linear Models in Process Engineering using Operator Inference.

Item is Released

show all hide all

Basic

show hide

Item Permalink: https://hdl.handle.net/21.11116/0000-000E-7BA8-B Version Permalink: https://hdl.handle.net/21.11116/0000-000E-7BA9-A

Genre: Preprint

Files

show Files

hide Files

:

2402.17698.pdf (Preprint), 4MB

View Save

File Permalink:
https://hdl.handle.net/21.11116/0000-000E-7BAA-9

Name:
2402.17698.pdf

Description:
File downloaded from arXiv at 2024-02-28 12:00

OA-Status:
Not specified

Visibility:
Public

MIME-Type / Checksum:
application/pdf / [MD5]

Technical Metadata:

View

Copyright Date:
-

Copyright Info:
-

License:
http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Locators

show

Creators

show

hide

Creators:
Gosea, Ion Victor¹, Author
Peterson, Luisa², Author
Goyal, Pawan Kumar¹, Author
Bremer, Jens³, Author
Sundmacher, Kai², Author
Benner, Peter¹, Author

Affiliations:
1Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738141
2Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738151
3Clausthal University of Technology, ou_persistent22

Content

show

Details

show

hide

Language(s):

Dates: Created: 2024-02-27

Publication Status: Not specified

Pages: -

Publishing info: -

Table of Contents: -

Rev. Type: -

Identifiers: arXiv: 2402.17698

Degree: -

Event

show

Legal Case

show

Project information

show

Source

show