CAREER: Fast Surrogate Modeling for Design under Uncertainty of Complex Engineering Systems

职业：复杂工程系统不确定性下设计的快速代理建模

• 批准号: 1454601
• 负责人: Alireza Doostan
• 金额: $ 50万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454601&HistoricalAwards=false
• 关键词: CAREER; Fast; Surrogate; Modeling; Design

This Faculty Early Career Development (CAREER) Program grant will establish an integrated research and education program motivated by the challenges associated with the development of predictive, simulation-based methods for the design and optimization of complex engineering system. Complex engineering systems often involve multiple physical phenomena at multiple scales. Such systems dominate current engineering interests, for example, as in the design and manufacturing of materials and energy storage systems. The dynamics of these systems are intrinsically variable due to, for instance, material properties or manufacturing imperfections, so that there is an imperative need to quantify the impact of such uncertainties for accurate performance prediction and design optimization. To this end and with the objective of advancing the current simulation technologies available for design and optimization, this award supports the development of a set of novel theories, algorithms, and software tools for fast characterization and propagation of uncertainty. The increasing significance and societal impact of predictive simulation capabilities in present and future design of engineering systems will additionally form the basis of an outreach and education effort to attract and engage future generations of engineers, especially from women and underrepresented minorities, entering and studying at the University of Colorado, Boulder. The approach for uncertainty characterization and propagation is based on new and scalable surrogate modeling 
schemes, together with effective computational tools for robust design and optimization of complex engineering systems. The idea of surrogate modeling is to construct an approximate (but inexpensive to evaluate) representation of the mapping between the parameters of a model and the performance objectives. This surrogate model is then used for robust design, optimization, sensitivity analysis, or decision making. To enable fast construction of surrogate models, novel deterministic and random sampling schemes along with model reduction approaches will be developed, in the context of sparse and low-rank approximations. The key to the scalability of the new algorithms is that they effectively and automatically identify the lower-dimensional manifold on which the possibly high-dimensional or non-smooth system solution exists. The surrogate modeling tools will be employed to better predict the reliability of lithium ion battery cells, and to facilitate the uncertainty-aware design of their electrodes.

该学院早期职业发展（CAREER）计划拨款将建立一个综合的研究和教育计划，该计划的动机是与复杂工程系统的设计和优化的预测性，基于模拟的方法的开发相关的挑战。复杂的工程系统往往涉及多个尺度上的多种物理现象。 这种系统主导了当前的工程利益，例如，在材料和能量存储系统的设计和制造中。这些系统的动态特性由于例如材料特性或制造缺陷而本质上是可变的，因此迫切需要量化这种不确定性的影响，以实现准确的性能预测和设计优化。为此，为了推进当前可用于设计和优化的仿真技术，该奖项支持开发一套新颖的理论，算法和软件工具，用于快速表征和传播不确定性。预测模拟能力在当前和未来工程系统设计中的重要性和社会影响日益增加，这将进一步形成推广和教育工作的基础，以吸引和吸引未来几代工程师，特别是来自女性和代表性不足的少数民族，进入并在博尔德的科罗拉多大学学习。 不确定性表征和传播的方法是基于新的和可扩展的代理建模#8232;计划，以及有效的计算工具，强大的设计和优化复杂的工程系统。代理建模的思想是构建模型参数和性能目标之间映射的近似（但评估成本较低）表示。然后，该代理模型用于稳健设计、优化、灵敏度分析或决策。为了使代理模型的快速建设，新的确定性和随机抽样计划沿着与模型简化方法将开发，在稀疏和低秩近似的上下文中。新算法的可扩展性的关键是，它们有效地自动识别可能存在高维或非光滑系统解的低维流形。替代建模工具将用于更好地预测锂离子电池的可靠性，并促进其电极的不确定性设计。