Yoevita Emeliana
Master’s Student · Postgraduate Physics Programme, Universitas PadjadjaranMaster’s Student · Universitas Padjadjaran
Works onDigital Rock PhysicsMachine Learning
6Published papers+1 under review
3Top tierQ1 / high-impact journals
1h-indexScopus
16CitationsAll time
About
Models fluid flow and effective transport properties in 2D/3D porous rocks with lattice Boltzmann and deep-learning surrogates such as residual attention U-Net. First author of the Residual Attention U-Net paper in Machine Learning: Science and Technology (2025), and lead reviewer of the Top Tier systematic review on lattice Boltzmann and neural networks for porous rocks.
Publications
Peer-reviewed papers and manuscripts written with the DRPRS Lab.
The Lattice Boltzmann Method for Multicomponent Flows with High Viscosity Ratios: A Comprehensive Review
A Dimensionless Morphological Framework for Permeability Prediction in Porous Rocks: Extending Kozeny–Carman with Coordination Number and Throat-Scale Corrections
In review
Explainable Machine Learning for Sandstone Rock Typing: Triple-Anchor Convergence with Closed-Form Permeability Laws
Integrating Lattice Boltzmann and Neural Networks for Modeling Transport Parameters in Porous Rocks: A Systematic Review
A Comparative Study of Three-Dimensional Flow Based, Geometric, and Empirical Tortuosity Models in Carbonate and Sandstone Reservoirs
Residual Attention U-Net for Predicting Fluid Dynamics and Permeability in Two-Dimensional Digital Porous Rocks
Co-authors in the lab
History
2025 – Present
Master of Science in Physics (M.Sc) · Fast Track
Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran
Research: lattice Boltzmann method and deep-learning surrogates for digital rock physics.
2021 – 2025
Bachelor of Science in Geophysics
Department of Geophysics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran