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
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2023
Authors
Babović, ZoranBajat, 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
Article (Published version)
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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 Algor...ithms), 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.…).
Keywords:
Computing paradigms / Artificial intelligence / Control flow / Data flow / Big dataSource:
Journal of Big Data, 2023, 10Publisher:
- Springer
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GraFarTY - JOUR AU - Babović, Zoran AU - Bajat, Branislav AU - Đokić, Vladan AU - Đorđević, Filip AU - Drašković, Dražen AU - Filipović, Nenad AU - Furht, Borko AU - Gačić, Nikola AU - Ikodinović, Igor AU - Ilić, Marija AU - Irfanoglu, Ayhan AU - Jelenković, Branislav AU - Kartelj, Aleksandar AU - Klimeck, Gerhard AU - Korolija, Nenad AU - Kotlar, Miloš AU - Kovačević, Miloš AU - Kuzmanović, Vladan AU - Marinković, Marko AU - Marković, Slobodan AU - Mendelson, Avi AU - Milutinović, Veljko AU - Nešković, Aleksandar AU - Nešković, Nataša AU - Mitić, Nenad AU - Nikolić, Boško AU - Novoselov, Konstantin AU - Prakash, Arun AU - Ratković, Ivan AU - Stojadinović, Zoran AU - Ustyuzhanin, Andrey AU - Zak, Stan PY - 2023 UR - https://grafar.grf.bg.ac.rs/handle/123456789/3113 AB - 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.…). PB - Springer T2 - Journal of Big Data T1 - 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 VL - 10 DO - 10.1186/s40537-023-00731-6 ER -
@article{ author = "Babović, Zoran and Bajat, Branislav and Đokić, Vladan and Đorđević, Filip and Drašković, Dražen and Filipović, Nenad and Furht, Borko and Gačić, Nikola and Ikodinović, Igor and Ilić, Marija and Irfanoglu, Ayhan and Jelenković, Branislav and Kartelj, Aleksandar and Klimeck, Gerhard and Korolija, Nenad and Kotlar, Miloš and Kovačević, Miloš and Kuzmanović, Vladan and Marinković, Marko and Marković, Slobodan and Mendelson, Avi and Milutinović, Veljko and Nešković, Aleksandar and Nešković, Nataša and Mitić, Nenad and Nikolić, Boško and Novoselov, Konstantin and Prakash, Arun and Ratković, Ivan and Stojadinović, Zoran and Ustyuzhanin, Andrey and Zak, Stan", year = "2023", 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.…).", publisher = "Springer", journal = "Journal of Big Data", 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", volume = "10", doi = "10.1186/s40537-023-00731-6" }
Babović, Z., Bajat, B., Đokić, V., Đorđević, F., Drašković, D., Filipović, N., Furht, B., Gačić, N., Ikodinović, I., Ilić, M., Irfanoglu, A., Jelenković, B., Kartelj, A., Klimeck, G., Korolija, N., Kotlar, M., Kovačević, M., Kuzmanović, V., Marinković, M., Marković, S., Mendelson, A., Milutinović, V., Nešković, A., Nešković, N., Mitić, N., Nikolić, B., Novoselov, K., Prakash, A., Ratković, I., Stojadinović, Z., Ustyuzhanin, A.,& Zak, S.. (2023). 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. in Journal of Big Data Springer., 10. https://doi.org/10.1186/s40537-023-00731-6
Babović Z, Bajat B, Đokić V, Đorđević F, Drašković D, Filipović N, Furht B, Gačić N, Ikodinović I, Ilić M, Irfanoglu A, Jelenković B, Kartelj A, Klimeck G, Korolija N, Kotlar M, Kovačević M, Kuzmanović V, Marinković M, Marković S, Mendelson A, Milutinović V, Nešković A, Nešković N, Mitić N, Nikolić B, Novoselov K, Prakash A, Ratković I, Stojadinović Z, Ustyuzhanin A, Zak S. 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. in Journal of Big Data. 2023;10. doi:10.1186/s40537-023-00731-6 .
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, "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" in Journal of Big Data, 10 (2023), https://doi.org/10.1186/s40537-023-00731-6 . .