ITR: DDDAS Generalized Polynomial Chaos: Parallel Algorithms for Modeling and Propagating Uncertainty in Physical and Biological Systems

EIA-0218142George E.Karniadakis Brown University ITR/DDDAS广义多项式混沌：物理和生物系统中不确定性的建模和传播的并行算法我们的目标是生物工程和纳米技术中的原型问题。这些问题的耦合性质和涉及的许多参数为评估新算法在从0.1到10亿个自由度的分辨率下的性能提供了很好的试验台。不确定性的来源可能是边界或初始条件、几何域、输运系数、力学性质以及其他外部强迫或体积源的不完全知识或波动。所提出的工作将产生重大而广泛的影响，因为它将在大规模模拟中建立复合误差条，并将使大规模物理和生物系统的数值随机模拟成为可能。它还将使许多其他领域受益，包括气候和网络/网络流量建模，在这些领域，目前的不确定性建模方法是不充分的。随机模拟的响应可以作为敏感性分析，潜在地指导实验工作和动态仪器。

EIA-0218142George E. Karniadakis Brown UniversityITR/DDDAS Generalized Polynomial Chaos: Parallel Algorithms for Modeling and Propagating Uncertainty in Physical and Biological SystemsThe applications we target are prototype problems in bioengineering and in nanotechnology. The coupled nature of such problems and the many parameters involved provide a good testbed for evaluating the performance of the new algorithms at resolutions from 0.1 to 1 billion degrees-of-freedom. The sources of uncertainty may be caused by incomplete knowledge or fluctuations in boundary or initial conditions, geometric domain, transport coefficients, mechanical properties, and other external forcing or volumetric sources.The proposed work will have significant and broad impact as it will establish a composite error bar in large-scale simulations and will enable numerical stochastic approaches to large-scale simulations of physical and biological systems. It will also benefit many other fields including climate and network/web traffic modeling, where current uncertainty modeling approaches are inadequate. Stochastically simulated responses can serve as sensitivity analysis that could potentially guide experimental work and dynamic instrumentation.