Fri, 28 Feb 2025
12:00
L5

Extreme horizons and Hitchin equations

Maciej Dunajski
(Cambridge)
Abstract
We establish the rigidity theorem for black hole extremal horizons, and prove that their compact cross-sections must admit a Killing vector field. The intrinsic Riemannian geometry of extremal horizons admits a quasi-Einstein structure. We shall discuss another class of such structures  corresponding to projective metrizability, where global results can be obtained. In this case the quasi-Einstein structure is governed by the Hitchin equations.
 

 

Physics-informed recovery of nonlinear residual stress fields in an inverse continuum framework
 Sanz-Herrera, J Goriely, A Journal of the Mechanics and Physics of Solids volume 200 (27 Feb 2025) doi:10.1016/j.jmps.2025.106079
Higher order Lipschitz Sandwich theorems
 Lyons, T McLeod, A Journal of the London Mathematical Society volume 111 issue 3 (07 Mar 2025) doi:10.1112/jlms.70121
Higher order Lipschitz Sandwich theorems
 Lyons, T McLeod, A Journal of the London Mathematical Society volume 111 issue 3 (07 Mar 2025) doi:10.1112/jlms.70121
Thu, 08 May 2025
14:00
(This talk is hosted by Rutherford Appleton Laboratory)

Multilevel Monte Carlo Methods with Smoothing

Aretha Teckentrup
(University of Edinburgh)
Abstract

Parameters in mathematical models are often impossible to determine fully or accurately, and are hence subject to uncertainty. By modelling the input parameters as stochastic processes, it is possible to quantify the uncertainty in the model outputs. 

In this talk, we employ the multilevel Monte Carlo (MLMC) method to compute expected values of quantities of interest related to partial differential equations with random coefficients. We make use of the circulant embedding method for sampling from the coefficient, and to further improve the computational complexity of the MLMC estimator, we devise and implement the smoothing technique integrated into the circulant embedding method. This allows to choose the coarsest mesh on the  first level of MLMC independently of the correlation length of the covariance function of the random  field, leading to considerable savings in computational cost.

 

 

Please note; this talk is hosted by Rutherford Appleton Laboratory, Harwell Campus, Didcot, OX11 0QX

 

 

 

Wasserstein distributional adversarial training for deep neural networks
 Bai, X He, G Jiang, Y Obloj, J (13 Feb 2025)
Gas-induced bulging in pouch-cell batteries: a mechanical model
 Giudici, A Chapman, S Please, C Journal of Power Sources volume 640 (18 Mar 2025) doi:10.1016/j.jpowsour.2025.236553
Gas-induced bulging in pouch-cell batteries: a mechanical model
 Giudici, A Chapman, S Please, C Journal of Power Sources volume 640 (18 Mar 2025) doi:10.1016/j.jpowsour.2025.236553
Multiple scales homogenisation of a porous viscoelastic material with rigid inclusions: application to lithium-ion battery electrodes
 Foster, J Galvis, A Protas, B Chapman, S Journal of the Mechanics and Physics of Solids volume 199 (28 Feb 2025) doi:10.1016/j.jmps.2025.106072
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