Deep learning-based reconstruction of the structure of heterogeneous composites from their temperature fields

A data driven artificial neural network model for predicting radiative properties of metallic packed beds

(2019) Hyun Hee Kang et al.   JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER

Machine learning subsurface flow equations from data

(2019) Haibin Chang et al.   COMPUTATIONAL GEOSCIENCES

Machine Learning for Fluid Mechanics

(2019) Steven L. Brunton et al.   Annual Review of Fluid Mechanics

Predicting the effective thermal conductivity of composites from cross sections images using deep learning methods

(2019) Qingyuan Rong et al.   COMPOSITES SCIENCE AND TECHNOLOGY

Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning

(2019) Serveh Kamrava et al.   TRANSPORT IN POROUS MEDIA

Predicting Effective Diffusivity of Porous Media from Images by Deep Learning

(2019) Haiyi Wu et al.   Scientific Reports

Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

(2018) Zijiang Yang et al.   COMPUTATIONAL MATERIALS SCIENCE

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

(2018) Tom Young et al.   IEEE Computational Intelligence Magazine

Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns

(2018) Krithika Manohar et al.   IEEE CONTROL SYSTEMS MAGAZINE

Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

(2018) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Recent advances in convolutional neural networks

(2018) Jiuxiang Gu et al.   PATTERN RECOGNITION

Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods

(2018) Han Wei et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

(2018) Rohit K. Tripathy et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Seeing permeability from images: fast prediction with convolutional neural networks

(2018) Jinlong Wu et al.   Science Bulletin

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media

(2018) Shaoxing Mo et al.   WATER RESOURCES RESEARCH

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

(2017) Vijay Badrinarayanan et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Deep learning

(2015) Yann LeCun et al.   NATURE

Sequential Monte Carlo methods for Bayesian elliptic inverse problems

(2015) Alexandros Beskos et al.   STATISTICS AND COMPUTING

Fast Feature Pyramids for Object Detection

(2014) Piotr Dollar et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE