A Deep Learning-Based Approach to Design Metasurfaces From Desired Far-Field Specifications

Niu, Chen; Phaneuf, Mario; Qiu, Tianke; Mojabi, Puyan

doi:10.1109/ojap.2023.3292108

Cited by 8 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different optimization techniques have been employed to design metasurfaces and their impact on performance has been studied as in [18,[20][21][22][23][24]. State-of-the-art multi-objective optimization algorithms have been used to realize metasurfaces that meet multiple design goals and achieve high performances in terms of reflection, transmission, polarization, angular, and frequency-dependent properties [25].…”

Section: Of 20mentioning

confidence: 99%

Prediction Enhancement of Metasurface Absorbers Design Using Adaptive Cascaded Deep Learning (Acdl) Model

Ajmi,

Bait-Suwailam,

Khriji

et al. 2024

Preprint

View full text Add to dashboard Cite

This paper presents a customized adaptive cascaded deep learning (ACDL) model for the design and performance prediction of metasurface absorbers. A multi-resonant metasurface absorber structure is introduced, with 10 target-driven design parameters. The proposed deep learning model takes advantage of cascading several sub-deep neural network (DNN) layers with forward noise mitigation capability. The inherent appearance of sparse data is completely dealt with in this work by proposing a trained adaptive selection technique. On the basis of the findings, the prediction response is quite fast and accurate enough to retrieve the design parameters of the studied metasurface absorber and with two sized patches of 4000 and 7000 datasets. The training loss taken form the second DNN of our proposed model shows logarithmic mean squared errors of 0.039 and 0.033 when using Keras and the adaptive method, respectively, with a dataset split of 4000. On the contrary, for a dataset split of 7000, the errors are 0.049 with Keras and 0.045 with the adaptive method. On the other hand, the validation loss is evaluated using the mean square error method, which results in a loss with 4000 datasets split with the Keras method of 0.044, while it is 0.020 with the adaptive method. When extending the dataset to 7000, the validation loss with the keras splitting method is 0.0073, while it is improved, reaching 0.006 with the proposed adaptive method, and achieving a prediction accuracy of 94%. This proposed deep learning model can be deployed in the design process and synthesis of multi-resonant metasurface absorber structures. The proposed model shows its advantages of making the design process more efficient in sparse dataset handling, efficient approach in multi-resonance metasurface data pre-processing, less time consuming, and computationally valuable.

show abstract

Section: Of 20mentioning

confidence: 99%

Prediction Enhancement of Metasurface Absorbers Design Using Adaptive Cascaded Deep Learning (Acdl) Model

Ajmi,

Bait-Suwailam,

Khriji

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Various optimization techniques have been extensively explored in the design of metasurfaces, with studies such as [16,[18][19][20][21][22] delving into their impact on performance. State-of-the-art multiobjective optimization algorithms, as evidenced in [23], have been used to achieve metasurfaces that meet multiple design goals, excelling in reflection, transmission, polarization, angular, and frequency-dependent properties.…”

Section: Introductionmentioning

confidence: 99%

Prediction Enhancement of Metasurface Absorber Design Using Adaptive Cascaded Deep Learning (ACDL) Model

Ajmi,

Bait-Suwailam,

Khriji

et al. 2024

Electronics

View full text Add to dashboard Cite

This paper presents a customized adaptive cascaded deep learning (ACDL) model for the design and performance prediction of metasurface absorbers. A multi-resonant metasurface absorber structure is introduced, with 10 target-driven design parameters. The proposed deep learning model takes advantage of cascading several sub-deep neural network (DNN) layers with forward noise mitigation capabilities. The inherent appearance of sparse data is dealt with in this work by proposing a trained data-adaptive selection technique. On the basis of the findings, the prediction response is quite fast and accurate enough to retrieve the design parameters of the studied metasurface absorber with two patches of 4000- and 7000-sample datasets. The training loss taken from the second DNN of our proposed model showed logarithmic mean squared errors of 0.039 and 0.033 when using Keras and the adaptive method, respectively, with a dataset split of 4000. On the contrary, for a dataset split of 7000, the errors were 0.049 with Keras and 0.045 with the adaptive method. On the other hand, the validation loss was evaluated using the mean square error method, which resulted in a loss of 0.044 with the 4000-sample datasets split with the Keras method, while this was 0.020 with the adaptive method. When extending the dataset to 7000 samples, the validation loss with the Keras splitting method was 0.0073, while it was improved, reaching 0.006, with the proposed adaptive method, and achieved a prediction accuracy of 94%. This proposed deep learning model can be deployed in the design process and synthesis of multi-resonant metasurface absorber structures. The proposed model shows the advantages of making the design process more efficient in sparse dataset handling, being an efficient approach in multi-resonance metasurface data pre-processing, being less time consuming, and being computationally valuable.

show abstract

Physics-driven unsupervised deep learning network for programmable metasurface-based beamforming

Bao,

Li,

Huang

et al. 2024

iScience

View full text Add to dashboard Cite

A Deep Learning-Based Approach to Design Metasurfaces From Desired Far-Field Specifications

Cited by 8 publications

References 39 publications

Prediction Enhancement of Metasurface Absorbers Design Using Adaptive Cascaded Deep Learning (Acdl) Model

Prediction Enhancement of Metasurface Absorbers Design Using Adaptive Cascaded Deep Learning (Acdl) Model

Prediction Enhancement of Metasurface Absorber Design Using Adaptive Cascaded Deep Learning (ACDL) Model

Physics-driven unsupervised deep learning network for programmable metasurface-based beamforming

Contact Info

Product

Resources

About