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Efficient Bayesian inference using adversarial machine learning and low-complexity surrogate models
Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2021-04-18 , DOI: 10.1016/j.compchemeng.2021.107322
Jonggeol Na , Ji Hyun Bak , Nikolaos V. Sahinidis

Bayesian inference is a key method for estimating parametric uncertainty from data. However, most Bayesian inference methods require the explicit likelihood function or many samples, both of which are unrealistic to provide for complex first-principles-based models. Here, we propose a novel Bayesian inference methodology for estimating uncertain parameters of computationally intensive first-principles-based models. Our approach exploits both low-complexity surrogate models and variational inference with arbitrarily expressive inference models. The proposed methodology indirectly predicts output responses and casts Bayesian inference as an optimization problem. We demonstrate its performance via synthetic problems, computational fluid dynamics, and kinetic Monte Carlo simulation to verify its applicability. This fast and reliable methodology enables us to capture multimodality and the shape of complicated posterior distributions with the quality of state-of-the-art Hamiltonian Monte Carlo methods but with much lower computation cost.



中文翻译:

使用对抗机器学习和低复杂度替代模型进行有效贝叶斯推理

贝叶斯推理是一种从数据估计参数不确定性的关键方法。但是,大多数贝叶斯推断方法都需要显式似然函数或许多样本,而这两种样本对于提供基于第一原理的复杂模型都是不现实的。在这里,我们提出了一种新颖的贝叶斯推理方法,用于估计基于计算密集型第一原理的模型的不确定参数。我们的方法利用低复杂度的代理模型和带有任意表达的推理模型的变异推理。所提出的方法间接地预测输出响应,并将贝叶斯推断转化为优化问题。我们通过综合问题,计算流体动力学和动力学蒙特卡洛模拟来证明其性能,以验证其适用性。

更新日期:2021-05-11
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