Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains

Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains

Abstract This article describes a teaching strategy that synergizes computing and management, aimed at the running of complex projects in industry and academia, in the areas of civil engineering, physics, geosciences, and a number of other related fields. The course derived from this strategy includ...

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Bibliographic Details
Main Authors: Zoran Babović, Branislav Bajat, Dusan Barac, Vesna Bengin, Vladan Đokić, Filip Đorđević, Dražen Drašković, Nenad Filipović, Stephan French, Borko Furht, Marija Ilić, Ayhan Irfanoglu, Aleksandar Kartelj, Milan Kilibarda, Gerhard Klimeck, Nenad Korolija, Miloš Kotlar, Miloš Kovačević, Vladan Kuzmanović, Jean-Marie Lehn, Dejan Madić, Marko Marinković, Miodrag Mateljević, Avi Mendelson, Fedor Mesinger, Gradimir Milovanović, Veljko Milutinović, Nenad Mitić, Aleksandar Nešković, Nataša Nešković, Boško Nikolić, Konstantin Novoselov, Arun Prakash, Jelica Protić, Ivan Ratković, Diego Rios, Dan Shechtman, Zoran Stojadinović, Andrey Ustyuzhanin, Stan Zak
Format: Article
Language:English
Published: SpringerOpen 2023-05-01
Series:Journal of Big Data
Subjects:
Education
Computing paradigms
Artificial intelligence
Big data
Nature-based construction
Online Access:https://doi.org/10.1186/s40537-023-00730-7

Internet

https://doi.org/10.1186/s40537-023-00730-7

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