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首页> 外文期刊>Applied Computational Electromagnetics Society journal >Efficient Bayesian Parameter Inversion Facilitated by Multi-Fidelity Modeling
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Efficient Bayesian Parameter Inversion Facilitated by Multi-Fidelity Modeling

机译：多保真建模促进了高效的贝叶斯参数反演

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摘要

We propose an efficient Bayesian parameter inversion technique that utilizes the implicit particle filter to characterize the posterior distribution, and a multi-scale surrogate modeling method called the proper orthogonal decomposition mapping method to provide high-fidelity solutions to the forward model by conducting only low-fidelity simulations. The proposed method is applied to the nonlinear Burgers equation, widely used to model electromagnetic waves, with stochastic viscosity and periodic solutions. We consider solving the equation with a coarsely-discretized finite difference scheme, of which the solutions are used as the low-fidelity solutions, and a Fourier spectral collocation method, which can provide high-fidelity solutions. The results demonstrate that the computational cost of characterizing the posterior distribution of viscosity is greatly reduced by utilizing the low-fidelity simulations, while the loss of accuracy is unnoticeable.
机译：我们提出了一种有效的贝叶斯参数反演技术，该技术利用隐式粒子滤波器表征后验分布，并提出了一种多尺度代理建模方法（称为适当的正交分解映射方法），该模型仅通过进行低阶拟合即可为正向模型提供高保真解。保真度模拟。该方法适用于非线性Burgers方程，广泛用于电磁波的建模，具有随机粘度和周期解。我们考虑用一个粗糙离散的有限差分方案求解该方程，该方案的解用作低保真解，而傅立叶谱配置方法可以提供高保真解。结果表明，利用低保真度模拟可大大降低表征粘度的后验分布的计算成本，而损失精度却不明显。

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