论文标题
学习和纠正非高斯模型错误
Learning and correcting non-Gaussian model errors
论文作者
论文摘要
所有离散的数值模型都包含建模误差 - 使用减少订单模型时，将会放大此现实。准确近似建模错误的能力为模型置信度提供了统计信息，并使用数值模型在预测，层析成像和信号处理中改善了框架的定量结果。除此之外，在许多旨在捕获复杂物理的条件的系统中引起的高度非线性和非高斯建模错误的补偿是历史上一项艰巨的任务。在这项工作中，我们通过提出一种能够准确近似和补偿增强直接和反向问题中这种建模错误的神经网络方法来应对这一挑战。使用模拟和实验数据通过不同的直接和反向问题产生的模拟和实验数据证明了该方法的生存能力。
All discretized numerical models contain modelling errors - this reality is amplified when reduced-order models are used. The ability to accurately approximate modelling 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 modelling 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 modelling 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.
