Collaborative Research: Innovative Integrative Strategies for Nonlinear Parametric Inversion

合作研究：非线性参数反演的创新综合策略

• 批准号: 1217156
• 负责人: Eric de Sturler
• 金额: $ 35.99万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217156&HistoricalAwards=false
• 关键词: Collaborative; Research; Innovative; Integrative; Strategies

The investigators aim to reduce drastically the costs of numerical inversion (as occurs, for example, in medical imaging) by blending new parametric level-set approaches and nonlinear least squares methods together with innovations in linear solvers, preconditioning, and model reduction of parameterized systems. Four strategies are combined to reduce the computation necessary while not degrading accuracy of the solution. First, the dimension of the inverse problem is drastically reduced by developing low-order parametric inversion methods replacing the usual voxel-based inversion. Second, the optimization underlying parametric inversion incorporates novel nonlinear least-squares solvers specifically designed to deal with ill-conditioned Jacobians. Third, the high cost of solving many large forward problems is reduced through new model reduction techniques that are particularly well-suited to the structure of the inverse problems under consideration. Fourth, the high costs of computing reduced models and solving forward problems is reduced by innovations in Krylov subspace recycling and efficient reuse of preconditioners for parameterized linear systems. The inverse problems studied here involve recovery of images describing how unknown quantities of diagnostic interest (such as electrical conductivity) are distributed throughout a given medium (such as human tissue or soil). These images can reveal the presence or absence of anomalies, such as tumors in human tissue or contaminant plumes in soil. The computational extraction of high quality images from noisy surface measurements in reasonable time is a very difficult task. As rapid advances in technology make it possible to take vastly more measurements, computational bottlenecks become ever more acute, impeding innovation in medical and other areas of imaging. This project aims to combine innovations in diverse fields within computational linear algebra, systems theory, and optimization to create dramatically improved strategies for image extraction.

研究人员的目标是通过将新的参数水平集方法和非线性最小二乘法与线性求解器，预处理和参数化系统模型简化的创新相结合，大幅降低数值反演的成本（例如，在医学成像中）。四种策略相结合，以减少必要的计算，而不降低精度的解决方案。首先，通过开发低阶参数反演方法取代通常的基于体素的反演，大大降低了反问题的维数。其次，参数反演的优化结合了专门设计用于处理病态雅可比矩阵的新型非线性最小二乘求解器。第三，解决许多大型正向问题的高成本是通过新的模型简化技术，特别是非常适合于所考虑的逆问题的结构减少。第四，通过Krylov子空间回收和参数化线性系统预条件子的有效重用，降低了计算简化模型和求解正问题的高成本。这里研究的逆问题涉及图像的恢复，描述了未知量的诊断兴趣（如电导率）是如何分布在整个给定的介质（如人体组织或土壤）。这些图像可以揭示异常的存在或不存在，例如人体组织中的肿瘤或土壤中的污染物羽流。在合理的时间内从噪声表面测量中计算提取高质量图像是一项非常困难的任务。随着技术的快速进步，可以进行更多的测量，计算瓶颈变得越来越严重，阻碍了医学和其他成像领域的创新。该项目旨在结合联合收割机在计算线性代数，系统理论和优化的不同领域的创新，创造显着改善的战略，图像提取。