Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Data-driven reduced order modeling for time-dependent problems
 
Loading...
Thumbnail Image
research article

Data-driven reduced order modeling for time-dependent problems

Guo, Mengwu  
•
Hesthaven, Jan S.  
October 1, 2018
Computer Methods in Applied Mechanics and Engineering

A data-driven reduced basis (RB) method for parametrized time-dependent problems is proposed. This method requires the offline preparation of a database comprising the time history of the full-order solutions at parameter locations. Based on the full-order data, a reduced basis is constructed by the proper orthogonal decomposition (POD), and the maps between the time/parameter values and the projection coefficients onto the RB are approximated as a regression model. With a natural tensor grid between the time and the parameters in the database, a singular-value decomposition (SVD) is used to extract the principal components in the data of projection coefficients. The regression functions are represented as the linear combinations of several tensor products of two Gaussian processes, one of time and the other of parameters. During the online stage, the solutions at new time/parameter locations in the domain of interest can be recovered rapidly as outputs from the regression models. Featuring a non-intrusive nature and the complete decoupling of the offline and online stages, the proposed approach provides a reliable and efficient tool for approximating parametrized time-dependent problems, and its effectiveness is illustrated by non-trivial numerical examples.

  • Files
  • Details
  • Metrics
Loading...
Thumbnail Image
Name

Data-driven ROM.pdf

Access type

openaccess

Size

2.88 MB

Format

Adobe PDF

Checksum (MD5)

2b0f6b48c0247fc05e7f0e47bbe0e076

Loading...
Thumbnail Image
Name

Document file.pdf

Access type

openaccess

Size

2.77 MB

Format

Adobe PDF

Checksum (MD5)

08db4bd334f72d74bac826d67268dfe2

Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés