Establishing a common database of ice experiments and using machine learning to understand and predict ice behavior

Enthalten in
Cold regions science and technology 
Volume
162
Start Page
56
End Page
73
Citation
Cold Regions Science and Technology (162): 56-73 (2019-06)
Publisher DOI
10.1016/j.coldregions.2019.02.007
Scopus ID
2-s2.0-85063941026
ArXiv ID
1812.03994v2
Ice material models often limit the accuracy of ice related simulations. The reasons for this are manifold, e.g. complex ice properties. One issue is linking experimental data to ice material modeling, where the aim is to identify patterns in the data that can be used by the models. However, numerous parameters that influence ice behavior lead to large, high dimensional data sets which are often fragmented. Handling the data manually becomes impractical. Machine learning and statistical tools are applied to identify how parameters, such as temperature, influence peak stress and ice behavior. To enable the analysis, a common and small scale experimental database is established.
Schlagworte
Physics - Data Analysis; Statistics and Probability
MLE@TUHH
DDC Class
530: Physics