| contributor author | Ozelim, Luan Carlos de Sena Monteiro | |
| contributor author | Casagrande, Michéle Dal Toé | |
| contributor author | Cavalcante, André Luís Brasil | |
| contributor author | Tang, Chong | |
| date accessioned | 2026-08-20T12:14:32Z | |
| date available | 2026-08-20T12:14:32Z | |
| date copyright | 2025/08/26 | |
| date issued | 2025 | |
| identifier other | AJRUA6.RUENG-1616.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4313270 | |
| description abstract | AbstractRecent advancements in deep learning have revolutionized constitutive model calibration
in geotechnical engineering by automatically identifying complex patterns in high-dimensional
data, enhancing accuracy, and reducing reliance on subjective ... | |
| publisher | American Society of Civil Engineers | |
| title | Bayesian-Optimized Physics-Informed Deep Autoencoders for Efficient Calibration of Geotechnical Constitutive Models: A NorSand Case Study | |
| type | Journal Article | |
| journal volume | 11 | |
| journal issue | 4 | |
| journal title | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering | |
| identifier doi | 10.1061/AJRUA6.RUENG-1616 | |
| journal fristpage | 04025078-1 | |
| journal lastpage | 04025078-15 | |
| page | 15 | |
| tree | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2025:;Volume ( 011 ):;issue: 004 | |
| contenttype | Fulltext | |
