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Enhancing multi-physics modelling with deep learning: Predicting permeability through structural discontinuities
Amanzhol Kubeyev
School of Energy, Geoscience, Infrastructure and Society
Research output
:
Contribution to journal
›
Article
›
peer-review
4
Citations (Scopus)
120
Downloads (Pure)
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Dive into the research topics of 'Enhancing multi-physics modelling with deep learning: Predicting permeability through structural discontinuities'. Together they form a unique fingerprint.
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INIS
modeling
100%
learning
100%
fractures
100%
permeability
100%
physics
100%
images
50%
fluids
50%
prediction
33%
surfaces
33%
velocity
33%
calculation methods
33%
data
16%
information
16%
values
16%
roughness
16%
interactions
16%
solids
16%
volume
16%
equations
16%
geometry
16%
rocks
16%
testing
16%
Engineering
Deep Learning
100%
Fluid Velocity
66%
Computation Time
66%
Subsurface
33%
Velocity Field
33%
Accurate Prediction
33%
Discretization
33%
Fracture Surface
33%
Multiscale
33%
Roughness Parameter
33%
Stokes Equation
33%
Learning Approach
33%
Limitations
33%
Numerical Modeling
33%
Thermal Fluid
33%
Earth and Planetary Sciences
Velocity Distribution
100%
Numerical Modeling
100%
Chemical Engineering
Deep Learning
100%