Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
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] Computational applications using data driven modeling in process Systems: A review
Modeling and optimization of various processes enable more efficient operations and better
planning activities for new process developments. With recent advances in computing …
planning activities for new process developments. With recent advances in computing …
A robust deep learning workflow to predict multiphase flow behavior during geological CO2 sequestration injection and Post-Injection periods
Simulation of multiphase flow in porous media is essential to manage the geologic CO 2
sequestration (GCS) process, and physics-based simulation approaches usually take …
sequestration (GCS) process, and physics-based simulation approaches usually take …
A physics-constrained deep learning model for simulating multiphase flow in 3D heterogeneous porous media
Physics-based simulators for multiphase flow in porous media emulate nonlinear processes
with coupled physics, and usually require extensive computational resources for software …
with coupled physics, and usually require extensive computational resources for software …
Physics-informed neural nets for control of dynamical systems
Physics-informed neural networks (PINNs) incorporate established physical principles into
the training of deep neural networks, ensuring that they adhere to the underlying physics of …
the training of deep neural networks, ensuring that they adhere to the underlying physics of …
A gradient-based deep neural network model for simulating multiphase flow in porous media
Simulation of multiphase flow in porous media is crucial for the effective management of
subsurface energy and environment-related activities. The numerical simulators used for …
subsurface energy and environment-related activities. The numerical simulators used for …
Physics-informed machine learning for noniterative optimization in geothermal energy recovery
Geothermal energy is clean, renewable, and cost-effective and its efficient recovery
management mandates optimizing engineering parameters while considering the …
management mandates optimizing engineering parameters while considering the …
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
We propose a solution strategy for parameter identification in multiphase thermo-hydro-
mechanical (THM) processes in porous media using physics-informed neural networks …
mechanical (THM) processes in porous media using physics-informed neural networks …