Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network

Nanoscale ◽  
2019 ◽  
Vol 11 (19) ◽  
pp. 9749-9755 ◽  
Author(s):  
Yingshi Chen ◽  
Jinfeng Zhu ◽  
Yinong Xie ◽  
Naixing Feng ◽  
Qing Huo Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inverse Design ◽  
On Demand ◽  
Optical Responses ◽  
Artificial Neural

We establish an adaptive batch-normalized neural network, aiming to implement smart and rapid inverse design for graphene-based metamaterials with on-demand optical responses.

Fully Solution-Processed Transparent Artificial Neural Network Using Drop-On-Demand Electrohydrodynamic Printing

2019 ◽  
Vol 11 (19) ◽  
pp. 17521-17530 ◽  
Author(s):  
Jason Yong ◽  
You Liang ◽  
Yang Yu ◽  
Basem Hassan ◽  
Md Sharafat Hossain ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solution Processed ◽  
On Demand ◽  
Drop On Demand ◽  
Electrohydrodynamic Printing ◽  
Artificial Neural

The use of genetic algorithm and self-updating artificial neural network for the inverse design of cabin environment

2016 ◽  
Vol 26 (3) ◽  
pp. 347-354 ◽  
Author(s):  
Tian-hu Zhang ◽  
Xue-yi You
Keyword(s):  
Neural Network ◽  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Network ◽  
Computational Cost ◽  
The Self ◽  
Inverse Design ◽  
Artificial Neural ◽  
The Impact

The inverse process of computational fluid dynamics was used to explore the expected indoor environment with the preset objectives. An inverse design method integrating genetic algorithm and self-updating artificial neural network is presented. To reduce the computational cost and eliminate the impact of prediction error of artificial neural network, a self-updating artificial neural network is proposed to realize the self-adaption of computational fluid dynamics database, where all the design objectives of solutions are obtained by computational fluid dynamics instead of artificial neural network. The proposed method was applied to the inverse design of an MD-82 aircraft cabin. The result shows that the performance of artificial neural network is improved with the increase of computational fluid dynamics database. When the number of computational fluid dynamics cases is more than 80, the success rate of artificial neural network increases to more than 40%. Comparing to genetic algorithm and computational fluid dynamics, the proposed hybrid method reduces about 53% of the computational cost. The pseudo solutions are avoided when the self-updating artificial neural network is adopted. In addition, the number of computational fluid dynamics cases is determined automatically, and the requirement of human adjustment is avoided.

scholarly journals Artificial Neural Network- (ANN-) Based Proxy Model for Fast Performances’ Forecast and Inverse Schedule Design of Steam-Flooding Reservoirs

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuhui Zhou ◽  
Yunfeng Xu ◽  
Xiang Rao ◽  
Yujie Hu ◽  
Deng Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inverse Design ◽  
Ann Model ◽  
Production Schedule ◽  
Steam Flooding ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Forward Looking

Steam flooding is one of the most effective and mature technology in heavy oil development. In this paper, a numerical simulation technology of steam flooding reservoir based on the finite volume method is firstly established. Combined with the phase change of steam phase, the fully implicit solution for steam flooding is carried out by using adaptive-time-step Newton iteration method. The Kriging method is used for stochastically to generate 4250 geological model samples by considering reservoir heterogeneity, and corresponding production schedule parameters are randomly given; then, these reservoir model samples are handled by the numerical simulation technology to obtain corresponding dynamic production data, which constitute the data for artificial neural network (ANN) training. By using the highly nonlinear global effect of artificial neural network and its powerful self-adaptive and self-learning functions, the forward-looking and inverse design ANN models of steam-flooding reservoirs are established, which provides a new method for rapid prediction of steam-flooding production performance and production schedule parameter design. In 4250 samples, the error of the forward-looking model is basically less than 0.1%, and the error of the inverse design model is generally less than 15%. It fully shows that the ANN models developed in this paper can quickly and effectively predict oil production and design production parameters and have an important guiding role in the implementation of the steam flooding technology. Finally, the forward-looking ANN model is applied to efficiently analyze the influencing factors of steam flooding process, and uncertainty analysis of the inverse design ANN model is conducted by Monte Carlo Simulation to illustrate its robustness. Besides, this paper may provide a reference for the application of neural network models to underground oil and gas reservoir, which is a typical invisible black box.

Nanophotonic Inverse Design Using Artificial Neural Network

2017 ◽  
Author(s):  
John Peurifoy ◽  
Yichen Shen ◽  
Yi Yang ◽  
Li Jing ◽  
Fidel Cano-Renteria ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inverse Design ◽  
Artificial Neural
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