| Autor: |
Haojie Lian, Jiaqi Wang, Leilei Chen, Shengze Li, Ruochen Cao, Qingyuan Hu, Peiyun Zhao |
| Předmět: |
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| Zdroj: |
CMES-Computer Modeling in Engineering & Sciences; 2024, Vol. 140 Issue 1, p1143-1163, 21p |
| Abstrakt: |
This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from2Dimages. This approach reconstructs color and density fields from2Dimages usingNeural Radiance Field (NeRF) and improves image quality using frequency regularization. The NeRF model is obtained via joint training ofmultiple artificial neural networks, whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel. In addition, customized physics-informed neural network (PINN) with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier- Stokes equations and convection-diffusion equations to reconstruct the velocity field. The velocity uncertainties are also evaluated through ensemble learning. The effectiveness of the proposed algorithm is demonstrated through numerical examples. The presentmethod is an important step towards downstream tasks such as reliability analysis and robust optimization in engineering design. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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