论文标题
基于神经网络的一部分倒反转的合奏Kalman滤波器
Ensemble Kalman filter for neural network based one-shot inversion
论文作者
论文摘要
我们研究了在机器学习中出现的新技术的使用。我们的方法用神经网络替代了复杂的前向模型，该神经网络在估计数据中的未知参数时，以单一的含义同时训练，即神经网络仅针对未知参数进行训练。通过建立与贝叶斯方法解决反问题的链接，开发了一个算法框架，以确保参数估计W.R.的可行性。到向前模型。我们提出了一种基于集合卡尔曼倒置的变体的有效，无衍生的优化方法。数值实验表明，基于神经网络的一击倒置的集合Kalman滤波器是一个有希望的方向，结合了优化和机器学习技术的反问题。
We study the use of novel techniques arising in machine learning for inverse problems. Our approach replaces the complex forward model by a neural network, which is trained simultaneously in a one-shot sense when estimating the unknown parameters from data, i.e. the neural network is trained only for the unknown parameter. By establishing a link to the Bayesian approach to inverse problems, an algorithmic framework is developed which ensures the feasibility of the parameter estimate w.r. to the forward model. We propose an efficient, derivative-free optimization method based on variants of the ensemble Kalman inversion. Numerical experiments show that the ensemble Kalman filter for neural network based one-shot inversion is a promising direction combining optimization and machine learning techniques for inverse problems.
