Quasiaccurately solvable quantum mechanics problems and the anharmonic oscillator problem
Vshivtsev, A Zhukovskii, V Potapov, R Starinets, A Russian Physics Journal volume 36 issue 2 161-172 (Feb 1993)
RG fixed points in supergravity duals of 4-d field theory and asymptotically AdS spaces
Porrati, M Starinets, A Physics Letters B volume 454 issue 1-2 77-83 (May 1999)
Vacuum polarization due to a non-Abelian spherically symmetric chromodynamic field at a finite temperature
Vshivtsev, A Zhukovskii, V Starinets, A Russian Physics Journal volume 35 issue 11 1049-1055 (Nov 1992)
Tue, 18 May 2021
14:30
Virtual

Numerical analysis of a topology optimization problem for Stokes flow

John Papadopoulos
(Mathematical Insittute)
Abstract

A topology optimization problem for Stokes flow finds the optimal material distribution of a Stokes fluid that minimizes the fluid’s power dissipation under a volume constraint. In 2003, T. Borrvall and J. Petersson [1] formulated a nonconvex optimization problem for this objective. They proved the existence of minimizers in the infinite-dimensional setting and showed that a suitably chosen finite element method will converge in a weak(-*) sense to an unspecified solution. In this talk, we will extend and refine their numerical analysis. We will show that there exist finite element functions, satisfying the necessary first-order conditions of optimality, that converge strongly to each isolated local minimizer of the problem.

[1] T. Borrvall, J. Petersson, Topology optimization of fluids in Stokes flow, International Journal for Numerical Methods in Fluids 41 (1) (2003) 77–107. doi:10.1002/fld.426.

 

A link for this talk will be sent to our mailing list a day or two in advance.  If you are not on the list and wish to be sent a link, please contact @email.

Ultra-fast super-resolution imaging of biomolecular mobility in tissues
Miller, H Cosgrove, J Wollman, A Toole, P Coles, M Leake, M 179747 (23 Aug 2017)

Deep learning has become an important topic across many domains of science due to its recent success in image recognition, speech recognition, and drug discovery. Deep learning techniques are based on neural networks, which contain a certain number of layers to perform several mathematical transformations on the input.

Tue, 18 May 2021
15:15
Virtual

Factors in randomly perturbed graphs

Amedeo Sgueglia
(LSE)
Further Information

Part of the Oxford Discrete Maths and Probability Seminar, held via Zoom. Please see the seminar website for details.

Abstract

We study the model of randomly perturbed dense graphs, which is the union of any $n$-vertex graph $G_\alpha$ with minimum degree at least $\alpha n$ and the binomial random graph $G(n,p)$. In this talk, we shall examine the following central question in this area: to determine when $G_\alpha \cup G(n,p)$ contains $H$-factors, i.e. spanning subgraphs consisting of vertex disjoint copies of the graph $H$. We offer several new sharp and stability results.
This is joint work with Julia Böttcher, Olaf Parczyk, and Jozef Skokan.

Arboreal categories and resources
Abramsky, S Reggio, L Proceedings of the 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021) volume 198 issue 2021 115:1-115:20 (02 Jul 2021)
Subscribe to