Author, Institution: Pijus Makauskas, Kaunas University of Technology
Science area, field of science: Natural Sciences, Informatics, N009
Research supervisor: Prof. Dr. Mayur Pal (Kaunas University of Technology, Natural Sciences, Informatics, N009)
Dissertation Defence Board of Informatics Science Field:
Prof. Dr. Hab. Rimantas Barauskas (Kaunas University of Technology, Natural Sciences, Informatics, N009) – chairperson
Prof. Dr. Dalia Čalnerytė (Kaunas University of Technology, Natural Sciences, Informatics, N009)
Prof. Dr. Florian Doster (Heriot-Watt university, United Kingdom, Natural Sciences, Informatics, N009)
Assoc. Prof. Dr. Alessio Fumagalli (Polytechnic University of Milan, Italy, Natural Sciences, Informatics, N009)
Prof. Dr. Andrius Kriščiūnas (Kaunas University of Technology, Natural Sciences, Informatics, N009)
Dissertation defence meeting will be at the Rectorate Hall of Kaunas University of Technology (K. Donelaičio 73-402, Kaunas)
The doctoral dissertation is available at the library of Kaunas University of Technology (Gedimino 50, Kaunas) and on the internet: P. Makauskas el. dissertation.pdf
© P. Makauskas, 2026 “The text of the thesis may not be copied, distributed, published, made public, including by making it publicly available on computer networks (Internet), reproduced in any form or by any means, including, but not limited to, electronic, mechanical or other means. Pursuant to Article 25(1) of the Law on Copyright and Related Rights of the Republic of Lithuania, a person with a disability who has difficulties in reading a document of a thesis published on the Internet, and insofar as this is justified by a particular disability, shall request that the document be made available in an alternative form by e-mail to doktorantura@ktu.lt.”
Annotation: Dissertation investigates numerical and neural network methods for modelling subsurface flows in porous media. It aims to improve the stability and consistency of numerical solutions under anisotropic permeability and misalignment between the grid and the principal permeability directions, and to assess neural networks for pressure-field approximation. The multipoint flux approximation (MPFA-O) scheme is investigated by comparing component-wise and tensor-consistent rotations of the permeability tensor. The approach is extended to two-phase saturation problems by coupling the pressure solution with flux reconstruction. Numerical experiments show that tensor-consistent rotation helps prevent nonphysical pressure oscillations and saturation-front distortions under the tested conditions. A convolutional neural network is also developed to predict pressure fields from permeability distributions; its accuracy and error trends under refinement of the medium structure are evaluated. Geological data processing, geostatistical estimation, flow simulation and neural approximation are combined into a workflow for preliminary assessment of geothermal resources in Lithuania. The results support more reliable subsurface flow modelling and comparison of promising geothermal sites while accounting for uncertainty in the input data.