HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910627775119360 |
|---|---|
| author | Bryutkin, Andrey Huang, Jiahao Deng, Zhongying Yang, Guang Schönlieb, Carola-Bibiane Aviles-Rivero, Angelica |
| author_facet | Bryutkin, Andrey Huang, Jiahao Deng, Zhongying Yang, Guang Schönlieb, Carola-Bibiane Aviles-Rivero, Angelica |
| contents | We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks. The framework uses graph transformers with modular input encoders to directly incorporate differential equation information into the solution process. This modularity enhances parameter correspondence control, making HAMLET adaptable to PDEs of arbitrary geometries and varied input formats. Notably, HAMLET scales effectively with increasing data complexity and noise, showcasing its robustness. HAMLET is not just tailored to a single type of physical simulation, but can be applied across various domains. Moreover, it boosts model resilience and performance, especially in scenarios with limited data. We demonstrate, through extensive experiments, that our framework is capable of outperforming current techniques for PDEs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03541 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HAMLET: Graph Transformer Neural Operator for Partial Differential Equations Bryutkin, Andrey Huang, Jiahao Deng, Zhongying Yang, Guang Schönlieb, Carola-Bibiane Aviles-Rivero, Angelica Machine Learning Numerical Analysis We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks. The framework uses graph transformers with modular input encoders to directly incorporate differential equation information into the solution process. This modularity enhances parameter correspondence control, making HAMLET adaptable to PDEs of arbitrary geometries and varied input formats. Notably, HAMLET scales effectively with increasing data complexity and noise, showcasing its robustness. HAMLET is not just tailored to a single type of physical simulation, but can be applied across various domains. Moreover, it boosts model resilience and performance, especially in scenarios with limited data. We demonstrate, through extensive experiments, that our framework is capable of outperforming current techniques for PDEs. |
| title | HAMLET: Graph Transformer Neural Operator for Partial Differential Equations |
| topic | Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2402.03541 |