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....
Most machine learning applications in Earth system modeling currently rely on gradient-based supervised learning. This imposes stringent constraints on the nature of the data used for training (typically, residual time tendencies are needed), and it complicates learning about the interactions between mac...
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...
In this paper, we propose an approach that uses machine learning techniques to identify rules driving the defeaturing step. The expertise knowledge is supposed to be embedded in a set of configurations that form the basis to develop the processes and find the rules. For this, we propose a ...
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
Enhancing gait assistance control robustness of a hip exosuit by means of machine learning. IEEE Robot. Autom. Lett. 7, 7566–7573 (2022). Article Google Scholar Cao, W., Chen, C., Hu, H., Fang, K. & Wu, X. Effect of hip assistance modes on metabolic cost of walking with a ...
In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling pr...
Simio supports training, testing, and embedding Deep Neural Network agents into Process Digital Twin models, along with bidirectional interaction with Machine Learning algorithms to enhance model intelligence, optimize results, and reduce execution run times. Simio also supports the import and direct use...
Does AVEVA Process Simulation incorporate machine learning or artificial intelligence? Is AVEVA Process Simulation available on the cloud? What is the difference between AVEVA PRO/II Simulation and AVEVA Process Simulation? Start your journey See AVEVA Process Simulation in action. ...