用条件归一化流模型高效预测纳米颗粒层的辐射特性。
Conditional Normalizing Flow Surrogate for Monte Carlo Prediction of Radiative Properties in Nanoparticle-Embedded Layers
- 基于条件归一化流学习输入参数与光学输出的联合分布。
- 预测反射率、吸收率和透射率误差小，且可量化不确定性。
- 适合需要高精度与可信度评估的纳米材料辐射仿真场景。
我们提出一种概率性、数据驱动的代理模型，用于预测嵌入纳米颗粒的散射介质的辐射特性。该模型采用条件归一化流，学习在给定吸收系数、散射系数、各向异性因子和颗粒尺寸分布等输入参数条件下，反射率、吸收率和透射率的条件分布。训练数据通过蒙特卡洛辐射传输模拟生成，光学性质基于米氏理论计算。与传统神经网络不同，该条件归一化流模型可输出完整的后验预测分布，实现精准预测与严谨的不确定性量化。结果表明，该模型具备高预测精度与可靠的不确定性估计，是辐射传输模拟的强大高效替代方案。
原文摘要 · Abstract (English)
We present a probabilistic, data-driven surrogate model for predicting the radiative properties of nanoparticle embedded scattering media. The model uses conditional normalizing flows, which learn the conditional distribution of optical outputs, including reflectance, absorbance, and transmittance, given input parameters such as the absorption coefficient, scattering coefficient, anisotropy factor, and particle size distribution. We generate training data using Monte Carlo radiative transfer simulations, with optical properties derived from Mie theory. Unlike conventional neural networks, the conditional normalizing flow model yields full posterior predictive distributions, enabling both accurate forecasts and principled uncertainty quantification. Our results demonstrate that this model achieves high predictive accuracy and reliable uncertainty estimates, establishing it as a powerful and efficient surrogate for radiative transfer simulations.
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