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
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 en...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer International Publishing
2023
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| Online Access: | https://hdl.handle.net/1721.1/150828 |
| _version_ | 1811076448361906176 |
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| author | 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 |
| author2 | Massachusetts Institute of Technology. Institute for Data, Systems, and Society |
| author_facet | Massachusetts Institute of Technology. Institute for Data, Systems, and Society 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 |
| author_sort | Babović, Zoran |
| collection | MIT |
| description | 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.…). |
| first_indexed | 2024-09-23T10:21:48Z |
| format | Article |
| id | mit-1721.1/150828 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:21:48Z |
| publishDate | 2023 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1508282024-01-12T20:19:44Z 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 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 Massachusetts Institute of Technology. Institute for Data, Systems, and Society 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.…). 2023-05-30T16:34:24Z 2023-05-30T16:34:24Z 2023-05-22 2023-05-28T03:14:24Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/150828 Journal of Big Data. 2023 May 22;10(1):73 PUBLISHER_CC en https://doi.org/10.1186/s40537-023-00731-6 Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer International Publishing Springer International Publishing |
| spellingShingle | 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 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 |
| 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://hdl.handle.net/1721.1/150828 |
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