Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks
<b>Background</b>: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human body. <b>Methods</b...
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MDPI AG
2024-11-01
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| Series: | Tomography |
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| Online Access: | https://www.mdpi.com/2379-139X/10/12/140 |
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| author | Theodoros Leontiou Anna Frixou Marios Charalambides Efstathios Stiliaris Costas N. Papanicolas Sofia Nikolaidou Antonis Papadakis |
| author_facet | Theodoros Leontiou Anna Frixou Marios Charalambides Efstathios Stiliaris Costas N. Papanicolas Sofia Nikolaidou Antonis Papadakis |
| author_sort | Theodoros Leontiou |
| collection | DOAJ |
| description | <b>Background</b>: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human body. <b>Methods</b>: In this study, we employed 3D convolutional neural networks (CNNs) to predict internal temperature fields. The network’s performance was evaluated under both ideal and non-ideal conditions, incorporating noise and background temperature variations. A physics-informed loss function embedding the heat equation was used in conjunction with statistical uncertainty during training to simulate realistic scenarios. <b>Results</b>: The CNN achieved high accuracy for small phantoms (e.g., 10 cm in diameter). However, under non-ideal conditions, the network’s predictive capacity diminished in larger domains, particularly in regions distant from the surface. The introduction of physical constraints in the training processes improved the model’s robustness in noisy environments, enabling accurate reconstruction of hot-spots in deeper regions where traditional CNNs struggled. <b>Conclusions</b>: Combining deep learning with physical constraints provides a robust framework for non-invasive thermal imaging and other applications requiring high-precision temperature field reconstruction, particularly under non-ideal conditions. |
| format | Article |
| id | doaj-art-337df041f7f542e8aa54218daf28e75e |
| institution | Kabale University |
| issn | 2379-1381 2379-139X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tomography |
| spelling | doaj-art-337df041f7f542e8aa54218daf28e75e2024-12-27T14:56:22ZengMDPI AGTomography2379-13812379-139X2024-11-0110121930194610.3390/tomography10120140Three-Dimensional Thermal Tomography with Physics-Informed Neural NetworksTheodoros Leontiou0Anna Frixou1Marios Charalambides2Efstathios Stiliaris3Costas N. Papanicolas4Sofia Nikolaidou5Antonis Papadakis6Department of Mechanical Engineering, Frederick University, Nicosia 1036, CyprusComputation-Based Science and Technology Research Center (CaSToRC), The Cyprus Institute, 20 Kavafi Street, Nicosia 2121, CyprusDepartment of Business Administration, Frederick University, Nicosia 1036, CyprusComputation-Based Science and Technology Research Center (CaSToRC), The Cyprus Institute, 20 Kavafi Street, Nicosia 2121, CyprusComputation-Based Science and Technology Research Center (CaSToRC), The Cyprus Institute, 20 Kavafi Street, Nicosia 2121, CyprusKYAMOS Ltd., 37 Polyneikis Street, Strovolos, Nicosia 2047, CyprusKYAMOS Ltd., 37 Polyneikis Street, Strovolos, Nicosia 2047, Cyprus<b>Background</b>: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human body. <b>Methods</b>: In this study, we employed 3D convolutional neural networks (CNNs) to predict internal temperature fields. The network’s performance was evaluated under both ideal and non-ideal conditions, incorporating noise and background temperature variations. A physics-informed loss function embedding the heat equation was used in conjunction with statistical uncertainty during training to simulate realistic scenarios. <b>Results</b>: The CNN achieved high accuracy for small phantoms (e.g., 10 cm in diameter). However, under non-ideal conditions, the network’s predictive capacity diminished in larger domains, particularly in regions distant from the surface. The introduction of physical constraints in the training processes improved the model’s robustness in noisy environments, enabling accurate reconstruction of hot-spots in deeper regions where traditional CNNs struggled. <b>Conclusions</b>: Combining deep learning with physical constraints provides a robust framework for non-invasive thermal imaging and other applications requiring high-precision temperature field reconstruction, particularly under non-ideal conditions.https://www.mdpi.com/2379-139X/10/12/140thermal tomographyconvolutional neural networksphysics-informed neural networks3D temperature fieldheat conductioninverse problems |
| spellingShingle | Theodoros Leontiou Anna Frixou Marios Charalambides Efstathios Stiliaris Costas N. Papanicolas Sofia Nikolaidou Antonis Papadakis Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks Tomography thermal tomography convolutional neural networks physics-informed neural networks 3D temperature field heat conduction inverse problems |
| title | Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks |
| title_full | Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks |
| title_fullStr | Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks |
| title_full_unstemmed | Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks |
| title_short | Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks |
| title_sort | three dimensional thermal tomography with physics informed neural networks |
| topic | thermal tomography convolutional neural networks physics-informed neural networks 3D temperature field heat conduction inverse problems |
| url | https://www.mdpi.com/2379-139X/10/12/140 |
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