کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6963415 | 1452284 | 2015 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Software framework for inverse modeling and uncertainty characterization
ترجمه فارسی عنوان
چارچوب نرم افزار برای مدل سازی معکوس و مشخصه عدم قطعیت
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزار
چکیده انگلیسی
Estimation of spatial random fields (SRFs) is required for predicting groundwater flow, subsurface contaminant movement, and other areas of environmental and earth sciences modeling. This paper presents an inverse modeling framework called MAD# for characterizing SRFs, which is an implementation of the Bayesian inverse modeling technique Method of Anchored Distributions (MAD). MAD# allows modelers to “wrap” simulation models using an extensible driver architecture that exposes model parameters to the inversion engine. MAD# is implemented in an open source software package with the goal of lowering the barrier to using inverse modeling in education, research, and resource management. MAD# includes an intentionally simple user interface for simulation configuration, external software integration, spatial domain and model output visualization, and evaluation of model convergence. Four test cases are presented demonstrating the novel functionality of this framework to apply inversion in order to calibrate the model parameters characterizing a groundwater aquifer.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 66, April 2015, Pages 98-109
Journal: Environmental Modelling & Software - Volume 66, April 2015, Pages 98-109
نویسندگان
Carlos A. Osorio-Murillo, Matthew W. Over, Heather Savoy, Daniel P. Ames, Yoram Rubin,