论文标题
基于方差的全球灵敏度分析的极限学习机
Extreme learning machines for variance-based global sensitivity analysis
论文作者
论文摘要
基于方差的全球灵敏度分析（GSA）可以在应用于复杂模型时提供大量信息。这种方法的著名阿喀琉斯脚跟是其计算成本，通常在实践中使其不可行。一个有吸引力的替代方法是分析替代模型的敏感性，目的是降低计算成本，同时保持足够的准确性。如果替代物的“简单”足以适应其SOBOL指数的分析计算，则GSA的成本实际上将降低为替代物的构建。我们提出了一类新的稀疏体重极端学习机（SW-ELMS），当在GSA的背景下被认为是替代的，它可以为其索博的指数提供分析公式，并且与标准榆树不同，它们会产生这些指数的准确近似值。通过现场和化学反应网络中的两个传统基准来说明这种方法的有效性。
Variance-based global sensitivity analysis (GSA) can provide a wealth of information when applied to complex models. A well-known Achilles' heel of this approach is its computational cost which often renders it unfeasible in practice. An appealing alternative is to analyze instead the sensitivity of a surrogate model with the goal of lowering computational costs while maintaining sufficient accuracy. Should a surrogate be "simple" enough to be amenable to the analytical calculations of its Sobol' indices, the cost of GSA is essentially reduced to the construction of the surrogate. We propose a new class of sparse weight Extreme Learning Machines (SW-ELMs) which, when considered as surrogates in the context of GSA, admit analytical formulas for their Sobol' indices and, unlike the standard ELMs, yield accurate approximations of these indices. The effectiveness of this approach is illustrated through both traditional benchmarks in the field and on a chemical reaction network.
