Machine learning in geo-and environmental sciences: From small to large scale
In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
Reconstruction, optimization, and design of heterogeneous materials and media: Basic principles, computational algorithms, and applications
M Sahimi, P Tahmasebi - Physics Reports, 2021 - Elsevier
Modeling of heterogeneous materials and media is a problem of fundamental importance to
a wide variety of phenomena with applications to many disciplines, ranging from condensed …
a wide variety of phenomena with applications to many disciplines, ranging from condensed …
Recent advances in multiscale digital rock reconstruction, flow simulation, and experiments during shale gas production
The complex and multiscale nature of shale gas transport imposes new challenges to the
already well-developed techniques for conventional reservoirs, especially digital core …
already well-developed techniques for conventional reservoirs, especially digital core …
Slice-to-voxel stochastic reconstructions on porous media with hybrid deep generative model
Abstract Three-dimensional (3D) microstructures are useful for studying the spatial
structures and physical properties of porous media. A number of stochastic reconstructions …
structures and physical properties of porous media. A number of stochastic reconstructions …
Multi-scale reconstruction of porous media from low-resolution core images using conditional generative adversarial networks
Various rocks such as carbonate, coal or shale contain both micro-and macro-pores. To
accurately predict the fluid flow and mechanical properties of these porous media, a multi …
accurately predict the fluid flow and mechanical properties of these porous media, a multi …
DeePore: A deep learning workflow for rapid and comprehensive characterization of porous materials
DeePore 2 is a deep learning workflow for rapid estimation of a wide range of porous
material properties based on the binarized micro–tomography images. By combining …
material properties based on the binarized micro–tomography images. By combining …
Striving to translate shale physics across ten orders of magnitude: What have we learned?
Shales will play an important role in the successful transition of energy from fossil-based
resources to renewables in the coming decades. Aside from being a significant source of …
resources to renewables in the coming decades. Aside from being a significant source of …
DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets
Microstructure reconstruction is an important and emerging field of research and an
essential foundation to improving inverse computational materials engineering (ICME) …
essential foundation to improving inverse computational materials engineering (ICME) …
Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design
Porous energy materials are essential components of many energy devices and systems,
the development of which have been long plagued by two main challenges. The first is the …
the development of which have been long plagued by two main challenges. The first is the …
Reconstruction of three-dimension digital rock guided by prior information with a combination of InfoGAN and style-based GAN
In digital rock physics, the study of physical parameters and flow characteristics of reservoirs
requires a wealth of three-dimension digital rock samples. However, traditional physical …
requires a wealth of three-dimension digital rock samples. However, traditional physical …