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Multiscale Physics-Informed solver for poromechanics
Processes in deformable and fractured porous media are characterized by multiple scales and uncertainties. The current simulation challenges is solving multiscale modeling with Artificial Intelligence incorporating the synthetic or experimental data from unresolved scales.
The master thesis aim to extend the existing framework of Physics-Informed Machine Learning to poromechanics. The thesis will be driven in international cooperation with Utah University (Multiphysics Lab).
The candidate need to have the following background skills:
- programming skill in Python;
- knowledge of finite element and partial differential equation;
- knowledge of deep learning (pyTorch/tensorflow);
- able to acquire fast main concepts of geoscience process and implement new algorithms
The master thesis aim to extend the existing framework of Physics-Informed Machine Learning to poromechanics. The thesis will be driven in international cooperation with Utah University (Multiphysics Lab). The candidate need to have the following background skills: - programming skill in Python; - knowledge of finite element and partial differential equation; - knowledge of deep learning (pyTorch/tensorflow); - able to acquire fast main concepts of geoscience process and implement new algorithms
What am I going to study? The goal of the project is to develop a Physics informed Deep Learning for porous media process.
What is the impact of this study? Publication in internation paper peer-reviewed, possibility to work with international university and placement as PhD in USA.
What am I going to study? The goal of the project is to develop a Physics informed Deep Learning for porous media process. What is the impact of this study? Publication in internation paper peer-reviewed, possibility to work with international university and placement as PhD in USA.
For any further details
baroli@aices.rwth-aachen.de; pania.newell@utah.edu
For any further details baroli@aices.rwth-aachen.de; pania.newell@utah.edu