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

Brown University, Providence RI

Investigators

Abstract

EIA-0218142 George E. Karniadakis Brown University ITR/DDDAS Generalized Polynomial Chaos: Parallel Algorithms for Modeling and Propagating Uncertainty in Physical and Biological Systems The 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.