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master's thesis
Strojno učenje za simulacije
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:123845
Rafaj, Damjan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Rafaj, D. (2023). Strojno učenje za simulacije (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:123845

Rafaj, Damjan. "Strojno učenje za simulacije." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:123845

Rafaj, Damjan. "Strojno učenje za simulacije." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:123845

Rafaj, D. (2023). 'Strojno učenje za simulacije', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 02 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:123845

Rafaj D. Strojno učenje za simulacije [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 December 02] Available at: https://urn.nsk.hr/urn:nbn:hr:168:123845

D. Rafaj, "Strojno učenje za simulacije", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:123845

Metadata
TitleStrojno učenje za simulacije
Title (english)Machine Learning for Simulation
AuthorDamjan Rafaj
MentorKrešimir Matković (mentor)
Committee memberKrešimir Matković (predsjednik povjerenstva)
Committee memberIgor Sunday Pandžić (član povjerenstva)
Committee memberMirko Sužnjević (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad istražuje metode nadziranog strojnog i dubokog učenja koje mogu ponuditi inverzni model kompleksnog simulacijskog sustava koji inače nije matematički izvediv. Inverzni model ključan je u analizi, objašnjavanju i optimiranju simulacijskog sustava. U ovom konkretnom slučaju koristi se u rješenju vizualne analitike za simulaciju klimatizacijskih sustava koja, osim metoda strojnog i dubokog učenja, obuhvaća i interaktivnu vizualizaciju.
Abstract (english)
This thesis explores supervised machine and deep learning methods that can offer an inverse model of a complex simulation system that is otherwise not mathematically feasible. The inverse model is crucial in the analysis, explanation and optimization of the simulation system. In this particular case, it is used in a visual analytics solution for a simulation of air conditioning systems, which, in addition to machine and deep learning methods, also includes interactive visualization.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:123845
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-10-11 10:17:48