Microstructure reconstruction via artificial neural networks: a combination of causal and non-causal approach
We investigate the applicability of artificial neural networks (ANNs) in reconstructing a sample image of a sponge-like microstructure. We propose to reconstruct the image by predicting the phase of the current pixel based on its causal neighbourhood, and subsequently, use a non-causal ANN model to...
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| Format: | Article |
| Language: | English |
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CTU Central Library
2022-03-01
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| Series: | Acta Polytechnica CTU Proceedings |
| Subjects: | |
| Online Access: | https://ojs.cvut.cz/ojs/index.php/APP/article/view/8104 |
| _version_ | 1811299627998117888 |
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| author | Kryštof Latka Martin Doškář Jan Zeman |
| author_facet | Kryštof Latka Martin Doškář Jan Zeman |
| author_sort | Kryštof Latka |
| collection | DOAJ |
| description | We investigate the applicability of artificial neural networks (ANNs) in reconstructing a sample image of a sponge-like microstructure. We propose to reconstruct the image by predicting the phase of the current pixel based on its causal neighbourhood, and subsequently, use a non-causal ANN model to smooth out the reconstructed image as a form of post-processing. We also consider the impacts of different configurations of the ANN model (e.g., the number of densely connected layers, the number of neurons in each layer, the size of both the causal and non-causal neighbourhood) on the models’ predictive abilities quantified by the discrepancy between the spatial statistics of the reference and the reconstructed sample. |
| first_indexed | 2024-04-13T06:38:39Z |
| format | Article |
| id | doaj.art-b66fe432c16c4a10ba5ce12478bc903e |
| institution | Directory Open Access Journal |
| issn | 2336-5382 |
| language | English |
| last_indexed | 2024-04-13T06:38:39Z |
| publishDate | 2022-03-01 |
| publisher | CTU Central Library |
| record_format | Article |
| series | Acta Polytechnica CTU Proceedings |
| spelling | doaj.art-b66fe432c16c4a10ba5ce12478bc903e2022-12-22T02:57:48ZengCTU Central LibraryActa Polytechnica CTU Proceedings2336-53822022-03-0134323710.14311/APP.2022.34.00325344Microstructure reconstruction via artificial neural networks: a combination of causal and non-causal approachKryštof Latka0Martin Doškář1Jan Zeman2Gymmázium Nový PORG, Pod Krcským lesem 25, 142 00 Prague 4, Czech RepublicCzech Technical University in Prague, Faculty of Civil Engineering, Department of Mechanics, Thákurova 7, 166 29 Prague 6, Czech RepublicCzech Technical University in Prague, Faculty of Civil Engineering, Department of Mechanics, Thákurova 7, 166 29 Prague 6, Czech RepublicWe investigate the applicability of artificial neural networks (ANNs) in reconstructing a sample image of a sponge-like microstructure. We propose to reconstruct the image by predicting the phase of the current pixel based on its causal neighbourhood, and subsequently, use a non-causal ANN model to smooth out the reconstructed image as a form of post-processing. We also consider the impacts of different configurations of the ANN model (e.g., the number of densely connected layers, the number of neurons in each layer, the size of both the causal and non-causal neighbourhood) on the models’ predictive abilities quantified by the discrepancy between the spatial statistics of the reference and the reconstructed sample.https://ojs.cvut.cz/ojs/index.php/APP/article/view/8104microstructure reconstructionneural networkcausal neighbourhoodnon-causal neighbourhood |
| spellingShingle | Kryštof Latka Martin Doškář Jan Zeman Microstructure reconstruction via artificial neural networks: a combination of causal and non-causal approach Acta Polytechnica CTU Proceedings microstructure reconstruction neural network causal neighbourhood non-causal neighbourhood |
| title | Microstructure reconstruction via artificial neural networks: a combination of causal and non-causal approach |
| title_full | Microstructure reconstruction via artificial neural networks: a combination of causal and non-causal approach |
| title_fullStr | Microstructure reconstruction via artificial neural networks: a combination of causal and non-causal approach |
| title_full_unstemmed | Microstructure reconstruction via artificial neural networks: a combination of causal and non-causal approach |
| title_short | Microstructure reconstruction via artificial neural networks: a combination of causal and non-causal approach |
| title_sort | microstructure reconstruction via artificial neural networks a combination of causal and non causal approach |
| topic | microstructure reconstruction neural network causal neighbourhood non-causal neighbourhood |
| url | https://ojs.cvut.cz/ojs/index.php/APP/article/view/8104 |
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