Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/1358
Title: Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems
Authors: Babović, Zoran
Bajat, Branislav
Đokić, Vladan
Đorđević, Filip
Drašković, Dražen
Filipović, Nenad
Furht, Borko
Gačić, Nikola
Ikodinović, Igor
Ilić, Marija
Irfanoglu, Ayhan
Jelenković, Branislav
Kartelj, Aleksandar 
Klimeck, Gerhard
Korolija, Nenad
Kotlar, Miloš
Kovačević, Miloš
Kuzmanović, Vladan
Marinković, Marko
Marković, Slobodan
Mendelson, Avi
Milutinović, Veljko
Nešković, Aleksandar
Nešković, Nataša
Mitić, Nenad 
Nikolić, Boško
Novoselov, Konstantin
Prakash, Arun
Ratković, Ivan
Stojadinović, Zoran
Ustyuzhanin, Andrey
Zak, Stan
Affiliations: Informatics and Computer Science 
Keywords: Artificial intelligence;Big data;Computing paradigms;Control flow;Data flow
Issue Date: 1-Dec-2023
Rank: M21a
Publisher: Springer
Journal: Journal of Big Data
Abstract: 
This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).
Description: 
The version of record of this article, first published in Journal of Big Data, is available online at Publisher’s website: http://dx.doi.org/10.1186/s40537-023-00731-6
URI: https://research.matf.bg.ac.rs/handle/123456789/1358
DOI: 10.1186/s40537-023-00731-6
Rights: Attribution 3.0 United States
Appears in Collections:Research outputs

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