자료유형 | 학위논문 |
---|---|
서명/저자사항 | Deep-Learning-Based Surrogate Modeling of Flow and Coupled Flow-Geomechanics for Data Assimilation in Subsurface Systems. |
개인저자 | Tang, Meng. |
단체저자명 | Stanford University. |
발행사항 | [S.l.]: Stanford University., 2021. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2021. |
형태사항 | 189 p. |
기본자료 저록 | Dissertations Abstracts International 83-05B. Dissertation Abstract International |
ISBN | 9798494455208 |
학위논문주기 | Thesis (Ph.D.)--Stanford University, 2021. |
일반주기 |
Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Advisor: Durlofsky, Louis;Tartakovsky, Daniel;Volkov, Oleg. |
이용제한사항 | This item must not be sold to any third party vendors. |
일반주제명 | Maps. Data processing. Algorithms. Permeability. Aquifers. Data assimilation. Computer science. Hydrologic sciences. Water resources management. Natural resource management. |
언어 | 영어 |
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