In the remainder part of this chapter, local-similarity-based porous structure reconstruction and numerical reconstruction algorithms are introduced respectively to demonstrate the applications of machine learning in pore-scale reservoir modelingdoi:10.1016/B978-0-12-820957-8.00006-XShuyu Sun...
Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both powerful quantum algorithms and advanced quantum devices....
Applications of machine learning techniques to a sensor-network-based prosthesis training system In the past, the utilization of the limb prosthesis has improved the daily life of amputees or patients with movement disorders. However, a leg-amputee has......
Adopting machine learning methods to evaluate the applications in simulations (Myllyaho et al., 2021; Wang & Zheng, 2013); e.g., Westera et al. (2018) used machine learning to validate automatic scoring prediction. Studying the phenomenon in the natural settings to develop useful insights; e...
Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been...
Heterogeneous catalysis is at the heart of chemistry. New theoretical methods based on machine learning (ML) techniques that emerged in recent years provide a new avenue to disclose the structures and reaction in complex catalytic systems. Here we review
With the rapid advancement in AI technology, theories such as simulation technology, operational optimization, and machine/statistical learning are now interweaving and influencing with each other in an unprecedented way. In futu...
Hybrid methods and combinations with artificial intelligence and machine learning open new possibilities as well. The ever-increasing availability of computational power and the availability of quantum computers make applications feasible that were previously beyond consideration. Simulation is pushing back the...
Moreover, the optimal structures were found to exhibit excellent flexibility under compression of around −65% without failure and can be used in many applications such as flexible strain sensors. Our results demonstrate the applicability of machine learning to the efficient development of new ...
and computational mechanics, the machine learning-based multiscale modeling and simulation is still at its infant stage. In this paper, we aim at astate-of-the-artreview on the machine learning-based multiscale modeling and simulation of materials, and its applications in composite homogenization,...