| contributor author | Chen, Xiao-Xuan | |
| contributor author | Zhang, Pin | |
| contributor author | Yu, Hai-Sui | |
| contributor author | Yin, Zhen-Yu | |
| contributor author | Sheil, Brian | |
| date accessioned | 2026-08-20T10:53:41Z | |
| date available | 2026-08-20T10:53:41Z | |
| date copyright | 2025/06/27 | |
| date issued | 2025 | |
| identifier other | JGGEFK.GTENG-13267.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4311406 | |
| 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 ... | |
| publisher | American Society of Civil Engineers | |
| title | Parsimonious Universal Function Approximator for Elastic and Elastoplastic Cavity Expansion Problems | |
| type | Journal Article | |
| journal volume | 151 | |
| journal issue | 9 | |
| journal title | Journal of Geotechnical and Geoenvironmental Engineering | |
| identifier doi | 10.1061/JGGEFK.GTENG-13267 | |
| journal fristpage | 04025093-1 | |
| journal lastpage | 04025093-14 | |
| page | 14 | |
| tree | Journal of Geotechnical and Geoenvironmental Engineering:;2025:;Volume ( 151 ):;issue: 009 | |
| contenttype | Fulltext | |