Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models
Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models
复制标题
使用异方差贝叶斯神经网络集成进行降阶火焰模型的数据同化
DOI：
10.1007/978-3-030-77977-1_33
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
M. Juniper
中科院分区：
文献类型：
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作者：
Maximilian L. Croci;Ushnish Sengupta;M. Juniper
.使用异方差贝叶斯神经网络（BayNNEs）的合奏水平集模型的参数推断。神经网络是在8500个模拟的170万个观测值的库上训练的，这些观测值是使用具有已知参数的模型获得的。集合产生样本的后验概率分布的参数，条件的观察，以及估计的不确定性参数。预测的参数和不确定性进行比较，使用集合卡尔曼滤波器推断。使用BayNNE方法推断的预期参数值一旦经过训练，就会与使用卡尔曼滤波器推断的参数值相匹配，但所需的时间和计算成本不到卡尔曼滤波器的百万分之一。该方法使得基于物理的模型能够从真实的实验图像中进行调整。
. The parameters of a level-set flame model are inferred using an ensemble of heteroscedastic Bayesian neural networks (BayNNEs). The neural networks are trained on a library of 1.7 million observations of 8500 simulations of the flame edge, obtained using the model with known parameters. The ensemble produces samples from the posterior probability distribution of the parameters, conditioned on the observations, as well as estimates of the uncertainties in the parameters. The predicted parameters and uncertainties are compared to those inferred using an ensemble Kalman filter. The expected parameter values inferred with the BayNNE method, once trained, match those inferred with the Kalman filter but require less than one millionth of the time and computational cost of the Kalman filter. This method enables a physics-based model to be tuned from experimental images in real time.