首页> 外文期刊>Computer Methods in Applied Mechanics and Engineering >Solving inverse problems in stochastic models using deep neural networks and adversarial training
【24h】

Solving inverse problems in stochastic models using deep neural networks and adversarial training

机译:利用深神经网络和对抗训练解决随机模型中的逆问题

获取原文
获取原文并翻译 | 示例

摘要

Inverse problems associated with stochastic models constitute a significant portion of scientific and engineering applications. In such cases the unknown quantities are distributions. The applicability of traditional methods is limited because of their demanding assumptions or prohibitive computational consumption; for example, maximum likelihood methods require closed-form density functions, and Markov Chain Monte Carlo needs a large number of simulations. We propose a new method that estimates the unknown distribution by matching the statistical properties between observed and simulated random processes. We leverage the expressive power of neural networks to approximate the unknown distribution and use a discriminative neural network for computing the statistical discrepancies between the observed and simulated random processes. We demonstrated numerically that the proposed methods can estimate both the model parameters and learn complicated unknown distributions. (C) 2021 Elsevier B.V. All rights reserved.
机译:与随机模型相关的逆问题构成了科学和工程应用的重要部分。在这种情况下,未知量是分布。传统方法的适用性是有限的,因为他们苛刻的假设或禁止的计算消费;例如,最大似然方法需要闭合形式的密度函数,并且马尔可夫链Monte Carlo需要大量的模拟。我们提出了一种新的方法,通过匹配观察和模拟随机过程之间的统计特性来估计未知分布。我们利用神经网络的表现力来近似未知的分布,并使用鉴别的神经网络来计算观察和模拟随机过程之间的统计差异。我们在数字上展示了所提出的方法可以估计模型参数并学习复杂的未知分布。 (c)2021 elestvier b.v.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号