Attention Residual U-Net for Three-Dimensional Fluid Flow Velocity Prediction in Single Fractures
In review
Fadhillah Akmal, M.Si
Master Alumnus · Department of Geophysics, Universitas PadjadjaranMaster Alumnus · Universitas Padjadjaran
Works onDigital Rock PhysicsMachine Learning
4Published papers+1 under review
2First-authorLead publications
3h-indexScopus
17CitationsScopus
About
Uses machine learning, lattice Boltzmann simulation, and image processing to estimate the physical parameters of 2D and 3D digital rocks and fractures. First author of the Applied Sciences (2024) paper on 3D fracture parameters, and co-author of several deep-learning and LBM studies on digital rock characterization.
Publications
Peer-reviewed papers written with the DRPRS Lab.
Estimating Two-Dimensional Physical Parameters of Digital Rocks using Deep Learning
DOI for Estimating Two-Dimensional Physical Parameters of Digital Rocks using Deep Learning10.1088/1402-4896/ad9d08
Application of Machine Learning for Estimating the Physical Parameters of Three-Dimensional Fractures
The Lattice Boltzmann Method and Image Processing Techniques for Effective Parameter Estimation of Digital Rock
Machine Learning Prediction of Tortuosity in Digital Rock
DOI for Machine Learning Prediction of Tortuosity in Digital Rock10.25299/jgeet.2023.8.02-2.13875
Co-authors in the lab
Irwan Ary Dharmawan
4 papers together
Aldenia Alexandra
3 papers together
Ardian Nurcahya
3 papers together
Intan Nurma Yulita
2 papers together
Fatimah Az-Zahra
1 paper together
Muhammad I'tikafi Khoirul Haq
1 paper together
History
2024 – 2025
Master of Science in Physics (M.Si) · Fast Track
Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran
Research: machine learning and lattice Boltzmann models to estimate digital rock parameters.
2020 – 2024
Bachelor of Science in Geophysics
Department of Geophysics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran