Master Thesis: Prediction of 3D mass transport properties from 2D data using
convolutional neural networks
We use both statistical methods and machine learning to understand the
relationship between the structure/geometry of porous materials and their mass
transport properties, i.e. diffusive transport and fluid flow. In a recent
project, we generated a large number of virtual materials structures and
computed diffusivity and fluid permeability using lattice Boltzmann methods.
The data set consists of 90,000 binary 3D arrays of size 192^3 and the
corresponding computed properties, to our knowledge the largest dataset ever
of this kind. We used both artificial neural networks (ANNs) and 3D
convolutional neural networks (CNNs) to perform nonlinear regression and
predict the mass transport properties with high accuracy. However, in many
practical cases, only 2D data is available. Therefore, it is of interest to
develop methods for prediction of 3D properties using only 2D data, e. g. a
single slice of the 3D arrays.
Prifling et al, 2021, to appear online soon, available on request
Röding et al, 2020, https: // www. nature.com/articles/s41598-020-72085-5
(similar earlier study)
The purpose of the proposed MSc project is to develop methods for
prediction of 3D mass transport properties from 2D data using 2D CNNs (and/or
possibly other machine learning-based regression methods depending on the
The project consists of implementing and benchmarking appropriate methods
for regression/prediction of mass transport properties. The project will give
you opportunities to investigate e. g. single-channel and multi-channel 2D
CNNs, data augmentation schemes, and hyperparameter optimization. You will
benefit from a good knowledge of statistics, spatial statistics, and physics
to understand the underlying problem and its relevance. Knowledge of a major
deep learning library (preferably TensorFlow/Keras, but it's up to you) is
very beneficial. You should be near completion of an MSc program in e. g.
applied mathematics/statistics, computer science, or physics. The duration of
the project is 20 weeks (30 hp).
Welcome with your application!
If this sounds interesting and you would like to know more, please contact
Magnus Röding, +46 10 516 66 59. Candidates are encouraged to send in their
application as soon as possible but at the latest December 12, 2921. Suitable
applicants will be interviewed as applications are received.
Visstidsanställning 3-6 månader
Student - examensarbete/praktik
+46 10 516 66 59
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