Efficient Generation of Multimodal Fluid Simulation Data
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916151385128960 |
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| author | Baieri, Daniele Crisostomi, Donato Esposito, Stefano Maggioli, Filippo Rodolà, Emanuele |
| author_facet | Baieri, Daniele Crisostomi, Donato Esposito, Stefano Maggioli, Filippo Rodolà, Emanuele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_06284 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Efficient Generation of Multimodal Fluid Simulation Data Baieri, Daniele Crisostomi, Donato Esposito, Stefano Maggioli, Filippo Rodolà, Emanuele Computational Physics Graphics Fluid Dynamics 68U20 I.2.6; I.3; I.6.3 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. |
| title | Efficient Generation of Multimodal Fluid Simulation Data |
| topic | Computational Physics Graphics Fluid Dynamics 68U20 I.2.6; I.3; I.6.3 |
| url | https://arxiv.org/abs/2311.06284 |