Deep learning-based reconstruction of the structure of heterogeneous composites from their temperature fields
Inverse problems involving transport phenomena are ubiquitous in engineering practice, but their solution is often challenging. In this work, we build a data-driven deep learning model to predict the heterogeneous distribution of circle-shaped fillers in two-dimensional thermal composites using the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2020-04-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0004631 |