Utilizing Experimental Data in a Model‐Free Data‐Driven Method: The Case of Thermal Conduction

ABSTRACT The study at hand investigates the applicability of experimental data within a model‐free data‐driven solver. The heat conduction equation is facilitated and solved by the data‐driven solver without the need for a constitutive law. At first, the results of an artificially created dataset are compared to a finite element solution to assess the quality of the approach. Subsequently, in‐situ data is processed to create a dataset for the data‐driven solver. This approach is validated by comparison to experimental data. The quality of the measured data is discussed, and challenges arising in utilizing experimental data are pointed out. Furthermore, an improved measuring concept for future experimental data is detailed and tested based on finite element simulations. Another finding is the ability of the data‐driven solver to implicitly model changing material characteristics.

Authors

Institutions

Publication Details

Journal
International Journal for Numerical Methods in Engineering
Published
2025-11-20
DOI
https://doi.org/10.1002/nme.70165
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Utilizing Experimental Data in a Model‐Free Data‐Driven Method: The Case of Thermal Conduction

Michael Kaliske, Laurent Stainier, Markus Oeser, Johannes Storm et al.
International Journal for Numerical Methods in Engineering
Model Reduction and Neural Networks
article

Utilizing Experimental Data in a Model‐Free Data‐Driven Method: The Case of Thermal Conduction

Michael Kaliske, Laurent Stainier, Markus Oeser, Johannes Storm, Jakob Platen, Atul Anantheswar, Felix Harazin, Michael Scherkenbach
article en

Abstract

ABSTRACT The study at hand investigates the applicability of experimental data within a model‐free data‐driven solver. The heat conduction equation is facilitated and solved by the data‐driven solver without the need for a constitutive law. At first, the results of an artificially created dataset are compared to a finite element solution to assess the quality of the approach. Subsequently, in‐situ data is processed to create a dataset for the data‐driven solver. This approach is validated by comparison to experimental data. The quality of the measured data is discussed, and challenges arising in utilizing experimental data are pointed out. Furthermore, an improved measuring concept for future experimental data is detailed and tested based on finite element simulations. Another finding is the ability of the data‐driven solver to implicitly model changing material characteristics.

International Journal for Numerical Methods in EngineeringVol. 126(22)
École Centrale de Nantes (FR), Centre National de la Recherche Scientifique (FR), Institut de Recherche en Génie Civil et Mécanique (FR), Federal Highway and Transport Research Institute (DE), Technische Universität Dresden (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 72%
Model Reduction and Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.