Leveraging machine learning in porous media
The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML),
has had a significant impact on engineering and the fundamental sciences, resulting in …
has had a significant impact on engineering and the fundamental sciences, resulting in …
Physics-informed graph convolutional neural network for modeling fluid flow and heat convection
This paper introduces a novel surrogate model for two-dimensional adaptive steady-state
thermal convection fields based on deep learning technology. The proposed model aims to …
thermal convection fields based on deep learning technology. The proposed model aims to …
Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review
A review of uncertainty quantification techniques is provided for a variety of situations
involving uncertainties in model inputs (independent variables). The situations of interest are …
involving uncertainties in model inputs (independent variables). The situations of interest are …
Leak detection and localization in water distribution networks using conditional deep convolutional generative adversarial networks
This paper explores the use of 'conditional convolutional generative adversarial
networks'(CDCGAN) for image-based leak detection and localization (LD&L) in water …
networks'(CDCGAN) for image-based leak detection and localization (LD&L) in water …
Physics-informed graph convolutional neural network for modeling geometry-adaptive steady-state natural convection
This paper presents a novel deep learning-based surrogate model for steady-state natural
convection problem with variable geometry. Traditional deep learning based surrogate …
convection problem with variable geometry. Traditional deep learning based surrogate …
A physics-informed neural networks modeling with coupled fluid flow and heat transfer–Revisit of natural convection in cavity
Z Hashemi, M Gholampour, MC Wu, TY Liu… - … Communications in Heat …, 2024 - Elsevier
The physics-informed neural networks (PINNs) method offers a mesh-free approach for
solving partial differential equations, converting the task into an optimization problem based …
solving partial differential equations, converting the task into an optimization problem based …
Dynamic compact thermal models for skin temperature prediction of portable electronic devices based on convolution and fitting methods
H Liu, J Yu, R Wang - International Journal of Heat and Mass Transfer, 2023 - Elsevier
In most portable electronic devices, besides the temperature of multiple heat sources, ie
junction temperature, the temperature of the enclosure, ie skin temperature, should also be …
junction temperature, the temperature of the enclosure, ie skin temperature, should also be …
A mixed pressure-velocity formulation to model flow in heterogeneous porous media with physics-informed neural networks
Abstract Current implementations of Physics Informed Neural Networks (PINNs) can
experience convergence problems in simulating fluid flow in porous media with highly …
experience convergence problems in simulating fluid flow in porous media with highly …
[HTML][HTML] Predicting multiphase flow behavior of methane in shallow unconfined aquifers using conditional deep convolutional generative adversarial network
Numerical modeling is an essential tool for geoscience applications involving multiphase
flow behavior. Performing numerical simulation is, however, computationally intensive due …
flow behavior. Performing numerical simulation is, however, computationally intensive due …
Modeling transient natural convection in heterogeneous porous media with Convolutional Neural Networks
Abstract Convolutional Neural Networks (CNNs) are gaining significant attention in
applications related to coupled flow and transfer processes in porous media, especially …
applications related to coupled flow and transfer processes in porous media, especially …