An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media
TLDR
The results show that TNNA‐AUS successfully reduces the inversion bias and improves the computational efficiency and inversion accuracy, compared with the global improvement strategy of adding training samples according to the prior distribution of model parameters.About:
This article is published in Water Resources Research.The article was published on 2021-11-10 and is currently open access. It has received 25 citations till now. The article focuses on the topics: Deep learning.read more
Citations
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An Efficient Approach for Automatic Complex Fractured Networks Parameter Inversion Based on Surrogate Model and Deep Reinforcement Learning
TL;DR: In this paper , a new deep reinforcement learning (DRL) based approach is proposed for automatic curve matching on complex fractured networks well-testing interpretation, based on twin delayed deep deterministic policy gradient (TD3) algorithm, the proposed DRL approach is successfully applied to automatic matching of complex fracture networks' well test curves.
Journal ArticleDOI
Sensitivity-dependent dynamic searching approach coupling multi-intelligent surrogates in homotopy mechanism for groundwater DNAPL-source inversion.
TL;DR: In this article , a sensitivity-dependent progressive optimization system embedding ensemble-learning technique is designed to avoid repetitive CPU-demanding model evaluations in Sobol' global sensitivity analysis and swarm intelligence optimization inverse modeling, Kriging, support vector regression (SVR), kernel extreme learning machine (KELM), and deep convolutional neural network (DCNN) are compared and ensembled to build an accurate surrogate of the numerical model.
Journal ArticleDOI
Application of different watershed units to debris flow susceptibility mapping: A case study of Northeast China
Jiangfeng Lv,Shengwu Qin,Junjun Chen,Shuangshuang Qiao,Jingyu Yao,Xiaolan Zhao,Rong Cao,J Yin +7 more
TL;DR: In this article , two types of watershed units (HWUs and CWUs) were divided and logistic regression, multilayer perceptron (MLP), classification and regression tree (CART), and Bayesian network (BN) were selected as the evaluation models.
Journal ArticleDOI
Artificial Intelligence Designer for Optical Fibers: Inverse Design of a Hollow-Core Anti-Resonant Fiber Based on a Tandem Neural Network
Fanchao Meng,Jinmin Ding,Yiming Zhao,Hongwei Liu,Wei-Zhi Su,Luyun Yang,Guangming Tao,Andrey D. Pryamikov,Xin Wang,Hongqian Mu,Yingli Niu,Jingwen He,Xinghua Zhang,Shuqin Lou,Xinzhi Sheng,Sheng Liang +15 more
TL;DR: In this article , an effective approach to overcome the non-uniqueness challenge of inverse designs is the tandem neural network (T-NN), which consists of a forward prediction model and an inverse design model, which are trained by different datasets.
An Efficient Approach for Automatic Complex Fractured Networks Parameter Inversion Based on Surrogate Model and Deep Reinforcement Learning
Zhiming Chen,Peng Dong,Dexuan Li +2 more
TL;DR: In this paper , a new deep reinforcement learning (DRL) based approach is proposed for automatic curve matching on complex fractured networks well-testing interpretation, based on twin delayed deep deterministic policy gradient (TD3) algorithm, the proposed DRL approach is successfully applied to automatic matching of complex fracture networks' well test curves.
References
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