C
Christoph Schwab
Researcher at ETH Zurich
Publications - 494
Citations - 19971
Christoph Schwab is an academic researcher from ETH Zurich. The author has contributed to research in topics: Finite element method & Discretization. The author has an hindex of 71, co-authored 473 publications receiving 17940 citations. Previous affiliations of Christoph Schwab include École Polytechnique Fédérale de Lausanne & Linköping University.
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Boundary Element Methods for Maxwell Transmission Problems in Lipschitz Domains
TL;DR: The Maxwell equations in a domain with Lipschitz boundary and the boundary integral operator A occuring in the Calderón projector are considered and an inf-sup condition for A is proved using a Hodge decomposition to prove quasioptimal convergence of the resulting boundary element methods.
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Sparse deterministic approximation of Bayesian inverse problems
TL;DR: In this article, a parametric deterministic formulation of Bayesian inverse problems with an input parameter from infinite-dimensional, separable Banach spaces is presented, and the sparsity of the posterior density in terms of the summability of the input data's coefficient sequence is analyzed.
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Boundary element methods for Maxwell's equations on non-smooth domains
TL;DR: On polyhedral surfaces, quasioptimal asymptotic convergence of these Galerkin boundary element methods is proved and a sharp regularity result for the surface multipliers on polyhedral boundaries with plane faces is established.
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Sparse adaptive Taylor approximation algorithms for parametric and stochastic elliptic PDEs
TL;DR: In this paper, an adaptive numerical algorithm for constructing a sequence of sparse polynomials that is proved to converge toward the solution with the optimal benchmark rate is presented, where the convergence rate in terms of N does not depend on the number of parameters in V, which may be arbitrarily large or countably infinite.
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Multi-level Monte Carlo finite volume methods for nonlinear systems of conservation laws in multi-dimensions
TL;DR: A novel load balancing procedure is presented that ensures scalability of the MLMC algorithm on massively parallel hardware and is applied to simulate uncertain solutions of the Euler equations and ideal magnetohydrodynamics (MHD) equations.