Related references
Note: Only part of the references are listed.Physics-informed dynamic mode decomposition
Peter J. J. Baddoo et al.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2023)
Improved training of physics-informed neural networks for parabolic differential equations with sharply perturbed initial conditions
Yifei Zong et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2023)
A CNN-based approach for upscaling multiphase flow in digital sandstones
Javad Siavashi et al.
FUEL (2022)
Influence of Heterogeneity on Carbonate Permeability Upscaling: A Renormalization Approach Coupled with the Pore Network Model
Haiyang Zhang et al.
ENERGY & FUELS (2022)
Generalizable Permeability Prediction of Digital Porous Media via a Novel Multi-Scale 3D Convolutional Neural Network
Mohamed Elmorsy et al.
WATER RESOURCES RESEARCH (2022)
Scientific Machine Learning Through Physics-Informed Neural Networks: Where we are and What's Next
Salvatore Cuomo et al.
JOURNAL OF SCIENTIFIC COMPUTING (2022)
Multiscale Digital Rock Imaging and Modeling for Measuring the Heterogeneous Carbonate and Conglomerate Permeability at the Laboratory Plug Scale
Qianghui Xu et al.
ENERGY & FUELS (2022)
Physics-informed neural networks (PINNs) for fluid mechanics: a review
Shengze Cai et al.
ACTA MECHANICA SINICA (2021)
A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries
Ali Kashefi et al.
PHYSICS OF FLUIDS (2021)
Upscaling the porosity-permeability relationship of a microporous carbonate for Darcy-scale flow with machine learning
H. P. Menke et al.
SCIENTIFIC REPORTS (2021)
Machine learning-accelerated computational fluid dynamics
Dmitrii Kochkov et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)
Learning dynamical systems from data: A simple cross-validation perspective, part I: Parametric kernel flows
Boumediene Hamzi et al.
PHYSICA D-NONLINEAR PHENOMENA (2021)
Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations
QiZhi He et al.
WATER RESOURCES RESEARCH (2021)
Simulating fluid flow in complex porous materials by integrating the governing equations with deep-layered machines
Serveh Kamrava et al.
NPJ COMPUTATIONAL MATERIALS (2021)
Physics-informed machine learning
George Em Karniadakis et al.
NATURE REVIEWS PHYSICS (2021)
Machine Learning for Fluid Mechanics
Steven L. Brunton et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 52 (2020)
Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
Nicholas Geneva et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
Georgios Kissas et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)
Machine learning in geo- and environmental sciences: From small to large scale
Pejman Tahmasebi et al.
ADVANCES IN WATER RESOURCES (2020)
Kernel Flows: From learning kernels from data into the abyss
Houman Owhadi et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
Computations of permeability of large rock images by dual grid domain decomposition
Ying Da Wang et al.
ADVANCES IN WATER RESOURCES (2019)
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Yinhao Zhu et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
Application of the renormalization group approach for permeability estimation in digital rocks
Shuaishuai Wei et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2019)
A survey on Image Data Augmentation for Deep Learning
Connor Shorten et al.
JOURNAL OF BIG DATA (2019)
Survey on deep learning with class imbalance
Justin M. Johnson et al.
JOURNAL OF BIG DATA (2019)
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
Alberto Fernandez et al.
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH (2018)
Scale up of pore-scale transport properties from micro to macro scale; network modelling approach
Farzad Bashtani et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2018)
The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data
Richard A. Bauder et al.
HEALTH INFORMATION SCIENCE AND SYSTEMS (2018)
Industrial applications of digital rock technology
Carl Fredrik Berg et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2017)
The impact of porous media heterogeneity on non-Darcy flow behaviour from pore-scale simulation
Bagus P. Muljadi et al.
ADVANCES IN WATER RESOURCES (2016)
HYBRID MULTISCALE FINITE VOLUME METHOD FOR ADVECTION-DIFFUSION EQUATIONS SUBJECT TO HETEROGENEOUS REACTIVE BOUNDARY CONDITIONS
David A. Barajas-Solano et al.
MULTISCALE MODELING & SIMULATION (2016)
An open-source toolbox for multiphase flow in porous media
P. Horgue et al.
COMPUTER PHYSICS COMMUNICATIONS (2015)
Resampling strategies for regression
Luis Torgo et al.
EXPERT SYSTEMS (2015)
Pore-scale imaging and modelling
Martin J. Blunt et al.
ADVANCES IN WATER RESOURCES (2013)
Digital rock physics benchmarks-part II: Computing effective properties
Heiko Andrae et al.
COMPUTERS & GEOSCIENCES (2013)
Computations of Absolute Permeability on Micro-CT Images
Peyman Mostaghimi et al.
MATHEMATICAL GEOSCIENCES (2013)
Lifetime of carbon capture and storage as a climate-change mitigation technology
Michael L. Szulczewski et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2012)
Lattice-Boltzmann studies of fluid flow in porous media with realistic rock geometries
Edo S. Boek et al.
COMPUTERS & MATHEMATICS WITH APPLICATIONS (2010)
New Renormalization Schemes for Conductivity Upscaling in Heterogeneous Media
M. R. Karim et al.
TRANSPORT IN POROUS MEDIA (2010)
Pore-network extraction from micro-computerized-tomography images
Hu Dong et al.
PHYSICAL REVIEW E (2009)
Analytical three-dimensional renormalization for calculating effective permeabilities
Christopher P. Green et al.
TRANSPORT IN POROUS MEDIA (2007)