| dc.contributor.author | Chen, Xiao-Xuan | |
| dc.contributor.author | Zhang, Pin | |
| dc.contributor.author | Yu, Hai-Sui | |
| dc.contributor.author | Yin, Zhen-Yu | |
| dc.contributor.author | Sheil, Brian | |
| dc.date.accessioned | 2026-08-20T10:53:41Z | |
| dc.date.available | 2026-08-20T10:53:41Z | |
| dc.date.copyright | 2025/06/27 | |
| dc.date.issued | 2025 | |
| dc.identifier.other | JGGEFK.GTENG-13267.pdf | |
| dc.identifier.uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4311406 | |
| dc.description.abstract | AbstractPhysics-informed neural networks (PINNs) have prevailed as an effective universal
function approximator to solve a wide range of governing partial differential equations
(PDEs) and ordinary differential equations (ODEs), including the ones in ... | |
| dc.publisher | American Society of Civil Engineers | |
| dc.title | Parsimonious Universal Function Approximator for Elastic and Elastoplastic Cavity Expansion Problems | |
| dc.type | Journal Article | |
| dc.journal.volume | 151 | |
| dc.journal.issue | 9 | |
| dc.journal.title | Journal of Geotechnical and Geoenvironmental Engineering | |
| dc.identifier.doi | 10.1061/JGGEFK.GTENG-13267 | |
| dc.journal.fristpage | 04025093-1 | |
| dc.journal.lastpage | 04025093-14 | |
| dc.page | 14 | |
| dc.tree | Journal of Geotechnical and Geoenvironmental Engineering:;2025:;Volume ( 151 ):;issue: 009 | |
| dc.contenttype | Fulltext | |
