Efficient lens design enabled by a multilayer perceptron-based machine learning scheme

Luo, Menglong; Bhandari, Bishal; Li, Hongliang; Aberdeen, Stuart; Lee, Sang‐Shin

doi:10.1016/j.ijleo.2022.170494

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…However, the conventional fully connected neural networks exhibit deficiency in predictive precision when applied to the inverse lens design. In our previous study 8 , a multilayer perceptron (MLP) architecture was attempted to serve as the framework of a fully connected neural network for the inverse lens design. The lens performance metrics were used as input features while the structural parameters were adopted as output labels for the MLP.…”

Section: Introductionmentioning

confidence: 99%

Inverse design of optical lenses enabled by generative flow-based invertible neural networks

Luo,

Lee

2023

Sci Rep

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Developing an optical geometric lens system in a conventional way involves substantial effort from designers to devise and assess the lens specifications. An expeditious and effortless acquisition of lens parameters satisfying the desired lens performance requirements can ease the workload by avoiding complex lens design process. In this study, we adopted the Glow, a generative flow model, which utilizes latent Gaussian variables to effectively tackle the issues of one-to-many mapping and information loss caused by dimensional disparities between high-dimensional lens structure parameters and low-dimensional performance metrics. We developed two lenses to tailor the vertical field of view and magnify the horizontal coverage range using two Glow-based invertible neural networks (INNs). By directly inputting the specified lens performance metrics into the proposed INNs, optimal inverse-designed lens specifications can be obtained efficiently with superb precision. The implementation of Glow-assisted INN approach is anticipated to significantly streamline the optical lens design workflows.

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Section: Introductionmentioning

confidence: 99%

Inverse design of optical lenses enabled by generative flow-based invertible neural networks

Luo,

Lee

2023

Sci Rep

Self Cite

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“…The prediction is calculated one-step ahead using inputs that are lagged time series observation s or other explanatory variables. Currently, multilayer perceptrons are still a popular data analysis tool, especially in the stu dy o f n o n -lin ear, real-time models, and are part of most Business Intelligence Platforms [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Time series forecasting with multilayer perceptrons

Botygin,

Тартаковский,

Шерстнёв

et al. 2023

29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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An implementation of a week-ahead air temperature and atmospheric pressure forecast using a multilayer percep tro n is presented (MLP). According to the specified meteorological parameters, data preparation, implementation and performance evaluation were performed for two MLP models. The MLP architecture was a s u perv ised feed -forward neural network with five hidden nodes and twenty iterations (repetitions). The obtained values of the ris k fu n ctio n (in this case, the standard deviation of the MSE) in both implementations are quite large.

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“…With reference to these research methods, it is advisable to use neural network for weight calculation. The multi-layer perceptron neural network has high precision and is more sensitive to weight calculation (Gurgel et al, 2022;Luo. et al, 2023), so it is suitable for quantitative characterization of the influencing factors of recovery efficiency.…”

Section: Introductionmentioning

confidence: 99%

Cluster analysis of carboniferous gas reservoirs and application of recovery prediction model

et al. 2023

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Since the discovery of the Carboniferous gas reservoirs in East Sichuan in 1977, after more than 40 years of development, most of the gas reservoirs have entered the middle and late stages of development. The gas reservoir is characterized by strong heterogeneity, large difference in permeability, and serious impact of water invasion in some blocks. Therefore, how to make a correct decision on gas field development and deployment is of vital importance. Combined with system clustering, BP neural network, correlation analysis and other methods, this paper first analyzes and calculates the static indicators of the Carboniferous gas reservoirs, and then divides the gas reservoirs into four categories using ward clustering method according to the calculated weight value, and determines the characteristics of each type of gas reservoirs using correlation coefficient analysis method. Finally, the recovery prediction model of each type of gas reservoir is established according to the BP neural network. The results indicate that: (1) The recovery rate prediction model can predict the trend of cumulative gas production changes, thereby obtaining the space for improving recovery rate, and the accuracy of the prediction results is high, which can be used as a reference for gas field planning. (2) The sub-active gas reservoirs with strong heterogeneous water bodies and the inactive gas reservoirs with low permeability water bodies have a certain space for enhanced oil recovery.

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Efficient lens design enabled by a multilayer perceptron-based machine learning scheme

Cited by 4 publications

References 34 publications

Inverse design of optical lenses enabled by generative flow-based invertible neural networks

Inverse design of optical lenses enabled by generative flow-based invertible neural networks

Time series forecasting with multilayer perceptrons

Cluster analysis of carboniferous gas reservoirs and application of recovery prediction model

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