Geometric Representation Learning for Accelerated Design Analysis in Data-Scarce Environments
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| Publicado en: | ProQuest Dissertations and Theses (2025) |
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Chen, Yu-hsuan | |
| 245 | 1 | |a Geometric Representation Learning for Accelerated Design Analysis in Data-Scarce Environments | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a Geometric representation learning can address challenges that were previously difficult for data-driven methods due to data scarcity. Geometry data scarcity can be mitigated through grammar- based modeling or modality conversion, while label scarcity can be tackled in two ways. First, when indirect, easily accessible labels are available, weakly supervised learning allows for the extraction of high-level design features. Second, in the complete absence of labels, inter- modality geometric pretraining improves design quantity estimation in few-shot scenarios. This approach is effective for tasks involving scalar values, temporal histories, and scalar fields. Furthermore, customized training strategies can be tailored to capture and process domain-specific geometries, such as thin shells and geometries with fine-scale details. | |
| 653 | |a Design | ||
| 653 | |a Mechanical engineering | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Computer science | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3231830826/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3231830826/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |