The solution of several operations research problems requires the creation of a quantitative
model. Sensitivity analysis is a crucial step in the model building and result communication …

Sensitivity analysis (SA) aims to identify the key parameters that affect model performance
and it plays important roles in model parameterization, calibration, optimization, and …

西北干旱区是对全球变化响应最敏感地区之一, 研究分析全球变暖背景下的西北干旱区水资源
问题, 对应对和适应未来气候变化带来的影响具有重要意义. 本文通过对西北干旱区气候变暖 …

Measuring variable importance for computational models or measured data is an important
task in many applications. It has drawn our attention that the variable importance analysis …

Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL),
have created tremendous excitement and opportunities in the earth and environmental …

Surrogate modeling, also called metamodeling, has evolved and been extensively used
over the past decades. A wide variety of methods and tools have been introduced for …

Variance based methods have assessed themselves as versatile and effective among the
various available techniques for sensitivity analysis of model output. Practitioners can in …

Mathematical modelers from different disciplines and regulatory agencies worldwide agree
on the importance of a careful sensitivity analysis (SA) of model-based inference. The most …

Complex mathematical and computational models are used in all areas of society and
technology and yet model based science is increasingly contested or refuted, especially …

Accuracy of results from mathematical and computer models of biological systems is often
complicated by the presence of uncertainties in experimental data that are used to estimate …