Luka Gradišar (Avtor), Matevž Dolenc (Avtor), Robert Klinc (Avtor)

Povzetek

Machine learned generative design is an extension of the generative design process, addressing its inherent limitations, particularly those of interoperability. The proposed approach uses machine learning-based surrogate models, trained on computational model data, to replicate design evaluations and integrate them into a common environment. In this way, design alternatives can be generated and tested that satisfy all design requirements and considerations. The effectiveness of this approach is demonstrated by the design and optimisation of the enclosure structure for the New Robotic Telescope. Its complexity is characterised by multiple operating states that the enclosure can assume, in particular the closed state and the opening/closing state, each of which has a different structural behaviour. Using our approach, the results from each state were replicated with machine learning models and combined into a single evaluation model. This resulted in findin g multiple solutions that outperformed the benchmark design. The results demonstrate not only the success of our method over conventional strategies, but also highlight its potential to redefine future design optimisation processes.

Ključne besede

računsko načrtovanje;generativno načrtovanje;strojno učenje;optimizacija;nadomestno modeliranje;computational design;generative design;machine learning;optimization;surrogate modelling;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FGG - Fakulteta za gradbeništvo in geodezijo
UDK: 004:624
COBISS: 180736259 Povezava se bo odprla v novem oknu
ISSN: 0926-5805
Št. ogledov: 46
Št. prenosov: 5
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: računsko načrtovanje;generativno načrtovanje;strojno učenje;optimizacija;nadomestno modeliranje;
Vrsta dela (COBISS): Članek v reviji
Strani: 16 str.
Zvezek: ǂVol. ǂ159, [article no.] 105284
Čas izdaje: Mar. 2024
DOI: 10.1016/j.autcon.2024.105284
ID: 23674333