基于异方差贝叶斯神经网络集成的本生火焰模拟参数在线推断
Online parameter inference for the simulation of a Bunsen flame using heteroscedastic Bayesian neural network ensembles
摘要：
本文提出了一种贝叶斯数据驱动的机器学习方法，用于管道预混火焰G方程模型参数的在线推断。异方差贝叶斯神经网络集成在LSGEN 2D（G方程求解器）中模拟的170万个火焰前沿库上进行训练，以学习给定观测值的模型参数的贝叶斯后验分布。然后，系综用于推断本生火焰实验的参数，使这些动态可以在LSGEN 2D中模拟。这允许计算火焰边缘的表面积变化，即热释放速率的代表。所提出的方法提供了廉价和在线的参数和不确定性估计匹配的结果与合奏卡尔曼滤波器，在较少的计算成本。这使得能够快速可靠地模拟燃烧过程。
英文摘要：
This paper proposes a Bayesian data-driven machine learning method for the online inference of the parameters of a G-equation model of a ducted, premixed flame. Heteroscedastic Bayesian neural network ensembles are trained on a library of 1.7 million flame fronts simulated in LSGEN2D, a G-equation solver, to learn the Bayesian posterior distribution of the model parameters given observations. The ensembles are then used to infer the parameters of Bunsen flame experiments so that the dynamics of these can be simulated in LSGEN2D. This allows the surface area variation of the flame edge, a proxy for the heat release rate, to be calculated. The proposed method provides cheap and online parameter and uncertainty estimates matching results obtained with the ensemble Kalman filter, at less computational cost. This enables fast and reliable simulation of the combustion process.
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