A critical review of physics-informed machine learning applications in subsurface energy systems
Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …
computer vision, natural language processing, and speech recognition. It can unravel …
[HTML][HTML] Physical activation functions (PAFs): An approach for more efficient induction of physics into physics-informed neural networks (PINNs)
In recent years, the evolution of Physics-Informed Neural Networks (PINNs) has reduced the
gap between Deep Learning (DL) based methods and analytical/numerical approaches in …
gap between Deep Learning (DL) based methods and analytical/numerical approaches in …
Application of Physics-Informed Neural Networks for Estimation of Saturation Functions from Countercurrent Spontaneous Imbibition Tests
In this work, physics-informed neural networks (PINNs) are used for history matching data
from core-scale countercurrent spontaneous imbibition (COUCSI) tests. To our knowledge …
from core-scale countercurrent spontaneous imbibition (COUCSI) tests. To our knowledge …
[HTML][HTML] History-Matching of imbibition flow in fractured porous media Using Physics-Informed Neural Networks (PINNs)
In this work, we propose a workflow based on physics-informed neural networks (PINNs) to
model multiphase fluid flow in fractured porous media. After validating the workflow in …
model multiphase fluid flow in fractured porous media. After validating the workflow in …
History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs)
We propose a workflow based on physics-informed neural networks (PINNs) to model
multiphase fluid flow in fractured porous media. After validating the workflow in forward and …
multiphase fluid flow in fractured porous media. After validating the workflow in forward and …
Physics-informed neural networks for modeling two-phase steady state flow with capillary heterogeneity at varying flow conditions
Multi-phase flow simulations in heterogeneous porous media are essential in many
applications, for example, CO 2 sequestration, enhanced oil and gas recovery, groundwater …
applications, for example, CO 2 sequestration, enhanced oil and gas recovery, groundwater …
Application of Physics-Informed Neural Networks for Estimation of Saturation Functions from Countercurrent Spontaneous Imbibition Tests
In this work, Physics-informed Neural Networks (PINNs) are employed for history-matching
data from core-scale countercurrent spontaneous imbibition (COUCSI) tests. To our …
data from core-scale countercurrent spontaneous imbibition (COUCSI) tests. To our …