TY  - 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 . .

TY  - JOUR
AU  - Babović, Zoran
AU  - Bajat, Branislav
AU  - Barac, Dusan
AU  - Bengin, Vesna
AU  - Đokić, Vladan
AU  - Đorđević, Filip
AU  - Drašković, Dražen
AU  - Filipović, Nenad
AU  - French, Stephan
AU  - Furht, Borko
AU  - Ilić, Marija
AU  - Irfanoglu, Ayhan
AU  - Kartelj, Aleksandar
AU  - Kilibarda, Milan
AU  - Klimeck, Gerhard
AU  - Korolija, Nenad
AU  - Kotlar, Miloš
AU  - Kovačević, Miloš
AU  - Kuzmanović, Vladan
AU  - Lehn, Jean-Marie
AU  - Madić, Dejan
AU  - Marinković, Marko
AU  - Mateljević, Miodrag
AU  - Mendelson, Avi
AU  - Mesinger, Fedor
AU  - Milovanović, Gradimir
AU  - Milutinović, Veljko
AU  - Mitić, Nenad
AU  - Nešković, Aleksandar
AU  - Nešković, Nataša
AU  - Nikolić, Boško
AU  - Novoselov, Konstantin
AU  - Prakash, Arun
AU  - Protić, Jelica
AU  - Ratković, Ivan
AU  - Rios, Diego
AU  - Shechtman, Dan
AU  - Stojadinović, Zoran
AU  - Ustyuzhanin, Andrey
AU  - Zak, Stan
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3114
AB  - 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 includes four parts: (a) Computing with a selected set of modern paradigms—the stress is on Control Flow and Data Flow computing paradigms, but paradigms conditionally referred to as Energy Flow and Diffusion Flow are also covered; (b) Project management that is holistic—the stress is on the wide plethora of issues spanning from the preparation of project proposals, all the way to incorporation activities to follow after the completion of a successful project; (c) Examples from past research and development experiences—the stress is on experiences of leading experts from academia and industry; (d) Student projects that stimulate creativity—the stress is on methods that educators could use to induce and accelerate the creativity of students in general. Finally, the article ends with selected pearls of wisdom that could be treated as suggestions for further elaboration.
PB  - Springer
T2  - Journal of Big Data
T1  - 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
VL  - 10
DO  - 10.1186/s40537-023-00730-7
ER  - 

@article{
author = "Babović, Zoran and Bajat, Branislav and Barac, Dusan and Bengin, Vesna and Đokić, Vladan and Đorđević, Filip and Drašković, Dražen and Filipović, Nenad and French, Stephan and Furht, Borko and Ilić, Marija and Irfanoglu, Ayhan and Kartelj, Aleksandar and Kilibarda, Milan and Klimeck, Gerhard and Korolija, Nenad and Kotlar, Miloš and Kovačević, Miloš and Kuzmanović, Vladan and Lehn, Jean-Marie and Madić, Dejan and Marinković, Marko and Mateljević, Miodrag and Mendelson, Avi and Mesinger, Fedor and Milovanović, Gradimir and Milutinović, Veljko and Mitić, Nenad and Nešković, Aleksandar and Nešković, Nataša and Nikolić, Boško and Novoselov, Konstantin and Prakash, Arun and Protić, Jelica and Ratković, Ivan and Rios, Diego and Shechtman, Dan and Stojadinović, Zoran and Ustyuzhanin, Andrey and Zak, Stan",
year = "2023",
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 includes four parts: (a) Computing with a selected set of modern paradigms—the stress is on Control Flow and Data Flow computing paradigms, but paradigms conditionally referred to as Energy Flow and Diffusion Flow are also covered; (b) Project management that is holistic—the stress is on the wide plethora of issues spanning from the preparation of project proposals, all the way to incorporation activities to follow after the completion of a successful project; (c) Examples from past research and development experiences—the stress is on experiences of leading experts from academia and industry; (d) Student projects that stimulate creativity—the stress is on methods that educators could use to induce and accelerate the creativity of students in general. Finally, the article ends with selected pearls of wisdom that could be treated as suggestions for further elaboration.",
publisher = "Springer",
journal = "Journal of Big Data",
title = "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",
volume = "10",
doi = "10.1186/s40537-023-00730-7"
}

Babović, Z., Bajat, B., Barac, D., Bengin, V., Đokić, V., Đorđević, F., Drašković, D., Filipović, N., French, S., Furht, B., Ilić, M., Irfanoglu, A., Kartelj, A., Kilibarda, M., Klimeck, G., Korolija, N., Kotlar, M., Kovačević, M., Kuzmanović, V., Lehn, J., Madić, D., Marinković, M., Mateljević, M., Mendelson, A., Mesinger, F., Milovanović, G., Milutinović, V., Mitić, N., Nešković, A., Nešković, N., Nikolić, B., Novoselov, K., Prakash, A., Protić, J., Ratković, I., Rios, D., Shechtman, D., Stojadinović, Z., Ustyuzhanin, A.,& Zak, S.. (2023). 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. in Journal of Big Data
Springer., 10.
https://doi.org/10.1186/s40537-023-00730-7

Babović Z, Bajat B, Barac D, Bengin V, Đokić V, Đorđević F, Drašković D, Filipović N, French S, Furht B, Ilić M, Irfanoglu A, Kartelj A, Kilibarda M, Klimeck G, Korolija N, Kotlar M, Kovačević M, Kuzmanović V, Lehn J, Madić D, Marinković M, Mateljević M, Mendelson A, Mesinger F, Milovanović G, Milutinović V, Mitić N, Nešković A, Nešković N, Nikolić B, Novoselov K, Prakash A, Protić J, Ratković I, Rios D, Shechtman D, Stojadinović Z, Ustyuzhanin A, Zak S. 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. in Journal of Big Data. 2023;10.
doi:10.1186/s40537-023-00730-7 .

Babović, Zoran, Bajat, Branislav, Barac, Dusan, Bengin, Vesna, Đokić, Vladan, Đorđević, Filip, Drašković, Dražen, Filipović, Nenad, French, Stephan, Furht, Borko, Ilić, Marija, Irfanoglu, Ayhan, Kartelj, Aleksandar, Kilibarda, Milan, Klimeck, Gerhard, Korolija, Nenad, Kotlar, Miloš, Kovačević, Miloš, Kuzmanović, Vladan, Lehn, Jean-Marie, Madić, Dejan, Marinković, Marko, Mateljević, Miodrag, Mendelson, Avi, Mesinger, Fedor, Milovanović, Gradimir, Milutinović, Veljko, Mitić, Nenad, Nešković, Aleksandar, Nešković, Nataša, Nikolić, Boško, Novoselov, Konstantin, Prakash, Arun, Protić, Jelica, Ratković, Ivan, Rios, Diego, Shechtman, Dan, Stojadinović, Zoran, Ustyuzhanin, Andrey, Zak, Stan, "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" in Journal of Big Data, 10 (2023),
https://doi.org/10.1186/s40537-023-00730-7 . .