AnisoGNN: graph neural networks generalizing to anisotropic properties of polycrystals
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911766817013760 |
|---|---|
| author | Hu, Guangyu Latypov, Marat I. |
| author_facet | Hu, Guangyu Latypov, Marat I. |
| contents | We present AnisoGNNs -- graph neural networks (GNNs) that generalize predictions of anisotropic properties of polycrystals in arbitrary testing directions without the need in excessive training data. To this end, we develop GNNs with a physics-inspired combination of node attributes and aggregation function. We demonstrate the excellent generalization capabilities of AnisoGNNs in predicting anisotropic elastic and inelastic properties of two alloys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16271 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AnisoGNN: graph neural networks generalizing to anisotropic properties of polycrystals Hu, Guangyu Latypov, Marat I. Materials Science We present AnisoGNNs -- graph neural networks (GNNs) that generalize predictions of anisotropic properties of polycrystals in arbitrary testing directions without the need in excessive training data. To this end, we develop GNNs with a physics-inspired combination of node attributes and aggregation function. We demonstrate the excellent generalization capabilities of AnisoGNNs in predicting anisotropic elastic and inelastic properties of two alloys. |
| title | AnisoGNN: graph neural networks generalizing to anisotropic properties of polycrystals |
| topic | Materials Science |
| url | https://arxiv.org/abs/2401.16271 |