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Learning and correcting non-Gaussian model errors
Journal of Computational Physics ( IF 4.1 ) Pub Date : 2021-01-29 , DOI: 10.1016/j.jcp.2021.110152
Danny Smyl , Tyler N. Tallman , Jonathan A. Black , Andreas Hauptmann , Dong Liu

All discretized numerical models contain modeling errors – this reality is amplified when reduced-order models are used. The ability to accurately approximate modeling errors informs statistics on model confidence and improves quantitative results from frameworks using numerical models in prediction, tomography, and signal processing. Further to this, the compensation of highly nonlinear and non-Gaussian modeling errors, arising in many ill-conditioned systems aiming to capture complex physics, is a historically difficult task. In this work, we address this challenge by proposing a neural network approach capable of accurately approximating and compensating for such modeling errors in augmented direct and inverse problems. The viability of the approach is demonstrated using simulated and experimental data arising from differing physical direct and inverse problems.



中文翻译:

学习和纠正非高斯模型错误

所有离散化的数值模型都包含建模误差-当使用降阶模型时,这种情况会被放大。准确地逼近建模误差的能力为模型的置信度提供了统计信息,并改善了在预测,层析成像和信号处理中使用数值模型的框架的定量结果。除此之外,在许多旨在捕获复杂物理学的病态系统中出现的,高度非线性和非高斯建模误差的补偿,在历史上是一项艰巨的任务。在这项工作中,我们通过提出一种神经网络方法来应对这一挑战,该方法能够准确地逼近和补偿正负反问题中的建模误差。

更新日期:2021-02-04
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