Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385080159> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W4385080159 abstract "Owing to ample potential and versatile capabilities of artificial intelligence to solve intricate scientific problems, two regression-based artificial neural network (ANN) models are proposed to design and optimize nano-structured meta-atoms. The proposed forward predicting ANN depicts that considering the complete structural and material information of the cylindrical nano-pillar meta-atoms could predict the corresponding electromagnetic (EM) response (amplitude and phase of transmission) with a mean squared error (MSE) as low as <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$mathbf{2.1}times mathbf{10}^{-mathbf{3}}$</tex> . Thus, it replaces the conventional EM simulations performed using high-end commercial software's, while significantly saving time and computational resources. Inverse design deep-learning model is also presented, which is connected with the pre-trained forward model and trained in a tandem architecture to provide an optimum set of dimensions and material, given the target response as its input. Furthermore, a comparative study regarding the number of hidden layers of the ANN and the amount of training dataset size is performed for the proposed forward and tandem inverse models to analyze the effect of considering extra underlying physics related information, i.e., wavelength regime and the EM spectral information. This study reveals that considering the extra information can lead to a significant reduction in the obtained MSE. Specifically, the proposed model could achieve a decent MSE even with a smaller amount of training dataset. Hence, the use of artificial intelligence models significantly reduces the training time and computational complexity of the proposed solution." @default.
- W4385080159 created "2023-07-23" @default.
- W4385080159 creator A5002095879 @default.
- W4385080159 creator A5006961690 @default.
- W4385080159 creator A5057380866 @default.
- W4385080159 creator A5087115411 @default.
- W4385080159 creator A5092271034 @default.
- W4385080159 date "2023-05-21" @default.
- W4385080159 modified "2023-09-25" @default.
- W4385080159 title "Reducing Complexity and data-set-Size Through Physics Inspired Tandem Neural Network" @default.
- W4385080159 cites W1998032073 @default.
- W4385080159 cites W2118761136 @default.
- W4385080159 cites W2962797490 @default.
- W4385080159 cites W2965595259 @default.
- W4385080159 cites W2977568909 @default.
- W4385080159 cites W2989293933 @default.
- W4385080159 cites W3000127088 @default.
- W4385080159 cites W3011023993 @default.
- W4385080159 cites W3098350469 @default.
- W4385080159 cites W3154153454 @default.
- W4385080159 cites W3200120172 @default.
- W4385080159 cites W4292517253 @default.
- W4385080159 doi "https://doi.org/10.1109/iscas46773.2023.10181524" @default.
- W4385080159 hasPublicationYear "2023" @default.
- W4385080159 type Work @default.
- W4385080159 citedByCount "0" @default.
- W4385080159 crossrefType "proceedings-article" @default.
- W4385080159 hasAuthorship W4385080159A5002095879 @default.
- W4385080159 hasAuthorship W4385080159A5006961690 @default.
- W4385080159 hasAuthorship W4385080159A5057380866 @default.
- W4385080159 hasAuthorship W4385080159A5087115411 @default.
- W4385080159 hasAuthorship W4385080159A5092271034 @default.
- W4385080159 hasConcept C105795698 @default.
- W4385080159 hasConcept C111335779 @default.
- W4385080159 hasConcept C11413529 @default.
- W4385080159 hasConcept C119857082 @default.
- W4385080159 hasConcept C127413603 @default.
- W4385080159 hasConcept C139945424 @default.
- W4385080159 hasConcept C146978453 @default.
- W4385080159 hasConcept C154945302 @default.
- W4385080159 hasConcept C177264268 @default.
- W4385080159 hasConcept C199360897 @default.
- W4385080159 hasConcept C207467116 @default.
- W4385080159 hasConcept C2524010 @default.
- W4385080159 hasConcept C2777814067 @default.
- W4385080159 hasConcept C33923547 @default.
- W4385080159 hasConcept C41008148 @default.
- W4385080159 hasConcept C50644808 @default.
- W4385080159 hasConceptScore W4385080159C105795698 @default.
- W4385080159 hasConceptScore W4385080159C111335779 @default.
- W4385080159 hasConceptScore W4385080159C11413529 @default.
- W4385080159 hasConceptScore W4385080159C119857082 @default.
- W4385080159 hasConceptScore W4385080159C127413603 @default.
- W4385080159 hasConceptScore W4385080159C139945424 @default.
- W4385080159 hasConceptScore W4385080159C146978453 @default.
- W4385080159 hasConceptScore W4385080159C154945302 @default.
- W4385080159 hasConceptScore W4385080159C177264268 @default.
- W4385080159 hasConceptScore W4385080159C199360897 @default.
- W4385080159 hasConceptScore W4385080159C207467116 @default.
- W4385080159 hasConceptScore W4385080159C2524010 @default.
- W4385080159 hasConceptScore W4385080159C2777814067 @default.
- W4385080159 hasConceptScore W4385080159C33923547 @default.
- W4385080159 hasConceptScore W4385080159C41008148 @default.
- W4385080159 hasConceptScore W4385080159C50644808 @default.
- W4385080159 hasLocation W43850801591 @default.
- W4385080159 hasOpenAccess W4385080159 @default.
- W4385080159 hasPrimaryLocation W43850801591 @default.
- W4385080159 hasRelatedWork W2961085424 @default.
- W4385080159 hasRelatedWork W2995227436 @default.
- W4385080159 hasRelatedWork W4225307033 @default.
- W4385080159 hasRelatedWork W4285260836 @default.
- W4385080159 hasRelatedWork W4286629047 @default.
- W4385080159 hasRelatedWork W4306321456 @default.
- W4385080159 hasRelatedWork W4306674287 @default.
- W4385080159 hasRelatedWork W4308950918 @default.
- W4385080159 hasRelatedWork W1629725936 @default.
- W4385080159 hasRelatedWork W4224009465 @default.
- W4385080159 isParatext "false" @default.
- W4385080159 isRetracted "false" @default.
- W4385080159 workType "article" @default.