CCS Reservoir Simulation using Graph Neural Networks: Building ML solutions for efficient CO2 subsurface modelling

Reducing cost and improving computability of reservoir simulation is an important goal in the process of enabling CCS (Carbon Capture \& Storage) as a large-scale technology for mitigating CO2 emissions. In terms of computation time data-driven approaches have potential to outweigh the performance of numerical reservoir simulators, learning...

Developing a level-1B qualifiable CNN for in-situ ultrasonic damage classification of aerospace composite structures: An in-depth evaluation on the end-to-end process of developing a data-driven tool

This paper examines the end-to-end development process for a Convolution Neural Network (CNN) based damage classification tool for ultrasonic inspection of aerospace-grade composite structures. The recent advent of Artificial Intelligence (AI) and Machine Learning (ML) has piqued the interest of the aerospace industry since it has the potential...

Gathering a Machine Learning dataset for object detection from a satellite-platform: On bandwidth-efficient gathering of a Machine Learning dataset for Object Detection with Faster-RCNN from a satellite-platform

In the past years, small Earth Observation (EO) satellites have become increasingly capable of taking high-resolution images at high sample rates. These images contain valuable information for different sectors, such as the agricultural and military sector. Furthermore they can contain important information about the climate and climate change....