发表机构
University of California at Davis（加州大学戴维斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于计算分类学（CT），以混凝土抗压强度系统为例，将实验设计（DoE）重新定义为寻找兼具类别代表性随机性且规避其余类别非线性的协变量子空间，为复杂系统DoE提供新视角。
AI 中文摘要
我们通过计算分类学（Computational Taxonomy, CT），基于严格重新定义的复杂系统动力学构成要素——随机性、非线性乃至类别，构建了数据驱动的分类层级结构，以此发展实验设计（Design of Experiment, DoE）。这是与无人工假设和结构的分类相反的探索，我们通过土木工程领域的复杂系统——混凝土抗压强度（Concrete Compressive Strength, CCS）来阐释DoE的这一新视角。CT首先构建分类层级结构，作为以树状几何框架呈现的异质性-同质性映射，用于表征CCS系统动力学。在该层级的每个内部节点，通过科学数据分析（Scientific Data Analysis, SDA）计算热图，揭示由协变量同质性的块结构所体现的局部性异质性，并标注响应的局部性划分。仅当到达该层级的每个末端节点时，响应和协变量两侧均实现同质性的一致性，由此在计算上识别并确认了一类有限样本性质。相比之下，当比较位于两个不同分支的类别时，响应与协变量同质性的不一致性清晰地体现了非线性。该层级明确映射了系统的随机性和非线性，为任何DoE探索提供科学基础。针对指定类别的实验设计（DoE）被重新定义为：寻找一个协变量子空间，该子空间既包含该类别代表性的随机性，同时又避免相对于其余类别的潜在非线性，这是DoE的一个全新主题。
英文摘要
Via Computational Taxonomy (CT), we develop Design of Experiment(DoE) based on rigorously redefined constituting ingredients of complex system dynamics: randomness, nonlinearity and even class, through a data-driven constructed Taxonomic Hierarchy. As an opposite quest of Classification without man-made assumptions and structures, we illustrate this new perspective of DoE through a civil engineering complex system: Concrete Compressive Strength (CCS). CT begins by building a Taxonomic Hierarchy as a heterogeneity-vs-homogeneity map framed with a tree geometry to represent CCS-system dynamics. At each internal node of this hierarchy, a heatmap is computed via Scientific Data Analysis (SDA) to reveal locality-embraced heterogeneity through block structured covariate homogeneity annotated with response's locality-split. Only arriving at each ending-node of this hierarchy, coherence of homogeneity is achieved on both response and covariate sides. As such a class of finite sample nature is computationally recognized and confirmed. In contrast, nonlinearity is evidently observed as incoherence of response-vs-covariate homogeneity when comparing two classes located on two distinct branches. This hierarchy explicitly maps out system's randomness and nonlinearity to serve as a scientific basis for any DoE quest. Design of Experiment (DoE) for any designated class is redefined as a search for a covariate subspace that embraces the class-representative randomness and at the same time avoids potential nonlinearities with respect to the rest of classes. This is a brand-new theme of DoE.
