Optimal closed-loop deep brain stimulation using multiple independently controlled contacts
Weerasinghe, G Duchet, B Bick, C Bogacz, R PLoS Computational Biology volume 17 issue 8 (06 Aug 2021)
“Hey, that's not an ODE”: Faster ODE Adjoints via Seminorms
Kidger, P Chen, R Lyons, T Proceedings of Machine Learning Research volume 139 5443-5452 (01 Jan 2021)
Neural SDEs as Infinite-Dimensional GANs
Kidger, P Foster, J Li, X Oberhauser, H Lyons, T Proceedings of Machine Learning Research volume 139 5453-5463 (01 Jan 2021)
Tue, 09 Nov 2021
14:30
L3

TBA

Fede Danieli
(University of Oxford)
Abstract

TBA

Tue, 09 Nov 2021
14:00
L3

TBA

Guiseppe Ughi
(University of Oxford)
Abstract

TBA

Tue, 23 Nov 2021
14:30
L3

A scalable and robust vertex-star relaxation for high-order FEM

Pablo Brubeck
(University of Oxford)
Abstract

The additive Schwarz method with vertex-centered patches and a low-order coarse space gives a p-robust solver for FEM discretizations of symmetric and coercive problems. However, for very high polynomial degree it is not feasible to assemble or factorize the matrices for each patch. In this work we introduce a direct solver for separable patch problems that scales to very high polynomial degree on tensor product cells. The solver constructs a tensor product basis that diagonalizes the blocks in the stiffness matrix for the internal degrees of freedom of each individual cell. As a result, the non-zero structure of the cell matrices is that of the graph connecting internal degrees of freedom to their projection onto the facets. In the new basis, the patch problem is as sparse as a low-order finite difference discretization, while having a sparser Cholesky factorization. We can thus afford to assemble and factorize the matrices for the vertex-patch problems, even for very high polynomial degree. In the non-separable case, the method can be applied as a preconditioner by approximating the problem with a separable surrogate. We apply this approach as a relaxation for the displacement block of mixed formulations of incompressible linear elasticity.

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