Author
Amir Salehi
Other affiliations: Iran University of Science and Technology, Stanford University, Bu-Ali Sina University
Bio: Amir Salehi is an academic researcher from Kharazmi University. The author has contributed to research in topics: Computer science & Workflow. The author has an hindex of 9, co-authored 31 publications receiving 270 citations. Previous affiliations of Amir Salehi include Iran University of Science and Technology & Stanford University.
Papers
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TL;DR: A novel hybrid approach is presented in which a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs.
55 citations
18 Feb 2013
46 citations
TL;DR: In this paper, the effects of supplementary cementitious materials on the temperature rising profile, heat evolution and early-age strength development of medium- and high-strength concrete were investigated, and the results showed that natural pozzolan particularly fly ash served to decrease the amplitude of peak temperature, delay the occurrence of the peak, and decrease the sharpness of the temperature rise profiles.
46 citations
23 Apr 2017
23 citations
TL;DR: A novel hybrid approach is presented in which a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs.
Abstract: Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome.
Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modeling
20 citations
Cited by
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31 Oct 2001
TL;DR: The American Society for Testing and Materials (ASTM) as mentioned in this paper is an independent organization devoted to the development of standards for testing and materials, and is a member of IEEE 802.11.
Abstract: The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.
3,792 citations
Journal Article•
1,200 citations
01 Jan 2002
237 citations
Book Chapter•
01 Dec 2001TL;DR: In this article, a summary of the issues discussed during the one day workshop on SVM Theory and Applications organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99) in Chania, Greece is presented.
Abstract: This chapter presents a summary of the issues discussed during the one day workshop on “Support Vector Machines (SVM) Theory and Applications” organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99) in Chania, Greece [19]. The goal of the chapter is twofold: to present an overview of the background theory and current understanding of SVM, and to discuss the papers presented as well as the issues that arose during the workshop.
170 citations