Efficient Generation of Multimodal Fluid Simulation Data

Autor:	Baieri, Daniele, Crisostomi, Donato, Esposito, Stefano, Maggioli, Filippo, Rodolà, Emanuele
Rok vydání:	2023
Předmět:	Physics - Computational Physics Computer Science - Graphics Physics - Fluid Dynamics 68U20 I.2.6 I.3 I.6.3
Druh dokumentu:	Working Paper
Popis:	In this work, we introduce an efficient generation procedure to produce synthetic multi-modal datasets of fluid simulations. The procedure can reproduce the dynamics of fluid flows and allows for exploring and learning various properties of their complex behavior, from distinct perspectives and modalities. We employ our framework to generate a set of thoughtfully designed training datasets, which attempt to span specific fluid simulation scenarios in a meaningful way. The properties of our contributions are demonstrated by evaluating recently published algorithms for the neural fluid simulation and fluid inverse rendering tasks using our benchmark datasets. Our contribution aims to fulfill the community's need for standardized training data, fostering more reproducibile and robust research. Comment: 10 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.06284 Zobrazit plný text záznamu View this record from Arxiv