{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:44:57Z","timestamp":1782841497667,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>This paper presents a hybrid approach for accelerating 3D smoke simulations in computer graphics using a lightweight deep neural network for volumetric upscaling. High-fidelity smoke simulation is computationally intensive due to the cubic growth of spatial resolution and the cost of solving the Navier-Stokes equations. Our method reduces computational demand by performing simulation at a lower resolution and subsequently reconstructing high-resolution volumetric fields using a compact neural network architecture. The proposed model focuses on recovering fine-scale turbulent structures and visual complexity while preserving the large-scale physical consistency provided by the underlying solver. Experimental results indicate substantial computational speedups relative to native high-resolution simulation, while qualitative comparisons suggest plausible recovery of fine-scale smoke detail. The proposed framework therefore targets interactive or near-real-time smoke preview workflows on commodity hardware, improving accessibility for iterative graphics production.<\/jats:p>","DOI":"10.7148\/2026-0533","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:14Z","timestamp":1782838514000},"page":"533-539","source":"Crossref","is-referenced-by-count":0,"title":["3d smoke upscaling using lightweight dnn"],"prefix":"10.7148","author":[{"given":"Michal","family":"Wieczorek","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Wozniak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:19Z","timestamp":1782838519000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0533_simo_ecms2026_0047.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0533","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}