"Collaborative Research"ITR-(ASE+EVS)-dmv+sim):Data Driven Simulation of the Subsurface: Optimization and Uncertainty Estimation

智力上的功绩。遥感在科学和工程问题中被用来推断材料的性质，当这些性质不能直接取样时。为了更好地理解和管理我们的环境，出于安全和经济的原因，在成像地下和基于遥感数据估计物理特性方面取得了很大进展。对环境修复和油藏生产目标的反复观察已成为协助管理决策的公认诊断工具。此外，改进的优化技术能够以容错和自适应的方式响应大型、多分辨率、不同的、动态的数据集，是有效地估计和最小化任何数据驱动的应用中的不确定性的基本要求。对这些问题的综合和有效的处理推动了本项目。召集起来的研究小组建议推进必要的数学、工程和计算基础，以加强我们的理解，并扩大在多个时间和空间尺度上支配地下现象的物理过程的预测能力。该项目的智力优势包括：(1)开发下一代精确的、多尺度的、耦合的化学、uid、地球力学和地球物理模拟器，用于模拟仪表化的地下环境；(2)大规模优化技术(基于全局和局部方法的混合)，以驱动可靠的决策和计算与数据之间的动态共生反馈；(3)部署自主网格中间件，为(1)和(2)提供足够的处理基础和数据管理服务。上述贡献的实现将导致数据驱动地下仿真框架(DDSSF)的产生。

Intellectual Merit. Remote sensing is employed in science and engineering problems to infer material properties when these properties can not be directly sampled. To better understand and manage our environment for safety and economic reasons, much progress has been made in imaging the subsurface and estimating physical properties based on remote sensing data. Repeated observations over targets for environmental remediation and reservoir production has become a recognized diagnostic tool for assisting management decisions. In addition, improved optimization techniques capable of responding to large, multi-resolution, disparate, dynamic datasets in a fault tolerant and adaptive fashion are a fundamental requirement for effectively estimating and minimizing the uncertainty in any data driven application. The integrated and e_ective treatment of these issues motivates the present project. The assembled research team proposes to advance the mathematical, engineering and computational foundations necessary to enhance our understanding and extend the predictive capabilities of the physical processes that govern the subsurface phenomena at multiple temporal and spatial scales. Target applications include management of aquifers for water resources, optimizing oil and gas production, and monitoring environmental risks e.g., at waste containment sites or arising from natural hazards.The intellectual merits of the project include: (1) development of the next generation of accurate, multi-scale, coupled chemical, uid, geomechanical, and geophysical simulators for modeling instrumented subsurface environments; (2) large scale optimization techniques (based on a hybridization of global and local approaches) to drive reliable decision-making and a dynamic symbiotic feedback between computation and data; (3) deployment of an autonomic Grid middleware for providing the adequate processing substrate and data management services for (1) and (2). The realization of the above contributions will result in the Data Driven Subsurface Simulation Framework (DDSSF).