CAREER: An Efficient Framework for Design and Modeling of Complex Computer Experiments

本研究的主要目标是开发一个有效的框架来设计和建模复杂的计算机实验，特别是那些具有多样化和高维输入和大量输出的实验。计算机实验，即使用模拟或数值代码的实验，已经被广泛地用作物理实验的替代，特别是在研究复杂现象时。研究人员引入了新的设计类别，这些设计可以有效地适应计算机实验中大量的定量和定性因素。研究人员还提出了一种新的自适应设计，该设计灵活且鲁棒，同时考虑了复杂系统中的不确定性。除了实验设计之外，还提出了一种新的采样/建模技术来降低计算机实验的计算复杂性和量化模型的不确定性，该方法适用于各种科学学科，并将对生物力学工程、系统生物学和环境科学等众多复杂实验领域的加速发现产生立竿见影的影响。特别是，所提出的建模技术可以极大地提高细胞黏附分析的效率和预测精度，这在肿瘤转移研究中具有重要作用。此外，所提出的方法还有助于分析气候变化、应对自然灾害和大流行性疾病传播的海量数据。在这项提案中概述的研究中纳入了一项教育计划，该计划强调对广大学生进行跨学科培训，并增加代表性不足群体的参与。建议的研究结果将被整合到罗格斯大学提供的本科生研究体验计划中。来自代表性不足群体的女性和本科生将被招募，并通过罗格斯大学非常成功的科学和工程研究计划积极参与PI的研究。将编写软件，让研究生和本科生有实践经验，在实际例子中实施新方法。

The primary objective of this research is to develop an efficient framework for design and modeling of complex computer experiments, especially those with diverse and high-dimensional inputs and massive outputs. Computer experiments, i.e., experiments using simulation or numerical codes, have been widely used as alternatives to physical experiments, especially for studying complex phenomena. The investigator introduces new classes of designs that can efficiently accommodate large numbers of both quantitative and qualitative factors in computer experiments. The investigator also proposes a new adaptive design that is flexible and robust, yet takes into account uncertainties in the complex systems. Apart from experimental design, a novel sampling/modeling technique is proposed to reduce computational complexity and quantify model uncertainty in the analysis of computer experiments with massive data.The proposed approaches are readily applicable to a variety of scientific disciplines and will have immediate impact on accelerating discoveries in numerous fields involving complex experiments like biomechanical engineering, systems biology, and environmental science. In particular, the proposed modeling techniques can dramatically enhance the efficiency and prediction accuracy in the analysis of cell adhesion, which plays an important role in tumor metastasis in cancer research. Moreover, the proposed methods can benefit the analysis of massive data for climate change, response to natural disasters, and the spread of pandemic disease. Integrated into the research outlined in this proposal is an education plan that emphasizes interdisciplinary training for a broad body of students and increasing participation from underrepresented groups. Results of the proposed research will be integrated into the Research Experience for Undergraduates program offered by Rutgers. Female and undergraduate students from underrepresented groups will be recruited and actively involved in the PI's research through Rutgers's highly successful Research in Science and Engineering program. Software will be written, which allows graduate and undergraduate students to have hands-on experience to implement the new methods on real examples.