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Sparse Tensor Approximations of High-Dimensional
and stochastic Partial Differential Equations

Funding

     
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European Research Council

     
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European Commission

Principal Investigator: Prof. C. Schwab
Researchers: Andrea Barth (Seminar for Applied Mathematics, ETH Zurich)
Phillip Grohs (Seminar for Applied Mathematics, ETH Zurich)
Markus Hansen (Seminar for Applied Mathematics, ETH Zurich)
Annika Lang (Seminar for Applied Mathematics, ETH Zurich)
Project period: 01.01.2010 to 31.12.2014 (60 months)
Call reference: ERC-2009-AdG_20090325

Summary

The present project addresses numerical analysis and algorithmic realization of sparse, adaptive
tensor product discretizations of partial di fferential equations (PDEs) in high dimensions
with stochastic data. The aim of the project is to develop mathematically founded adaptive
algorithms which are based on sparse tensorization of hierarchic Riesz bases or frames. These
will be hierarchic multilevel bases in the physical domain, either Finite Element wavelet type
bases or hierarchical, multilevel Finite Element frames. In the parameter domains corresponding
either to random inputs or to phase spaces in transport problems, spectral representations
of "polynomial chaos" type shall be employed. The mathematical aim is to obtain for broad
classes of elliptic and parabolic and certain hyperbolic PDEs on high or possibly infi nite dimensional
parameter spaces adaptive, deterministic and dimension independent numerical solution
methods with convergence rates superior to those afforded by sampling Methods, in terms of
accuracy vs. complexity. Algorithmic work in the project will address design of datastructures
with minimal overhead for the efficient realization of the sparse tensor approximations.
Applications include space-time adaptive solvers for elliptic, parabolic and certain parametric
hyperbolic PDEs. Applications include numerical solution of multiscale models in homogenization,
macro-micro polymer models in material science, stochastic di fferential equations arising
in bioengineering, Uncertainty Quanti cation and economics.
Mathematically, new results are expected on nonlinear approximate spectral representations
of nonstationary random fi elds, scale-resolving discretizations of multiscale elliptic and parabolic
problems featuring complexity independent of the number of scales. The project will be in
collaboration with coworkers in France, Germany, Singapore, UK, and The Netherlands. The
project involves mentoring postdocs and predocs who will be actively involved in all aspects of
the research, as well as a sustained graduate teaching component.

Research Reports

2011

2010

Contact

Prof. Christoph Schwab

 

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