L3

Atom Probe Tomography is a powerful 3D mass spectrometry technique. By pulsing the sample apex with an electric field, surface atoms are ionised and collected by a detector. A 3D image of estimated initial ion positions is constructed via an image reconstruction protocol. Current protocols assume ion trajectories follow a stereographic projection. However, this method assumes a hemispherical sample apex that fails to account for varying material ionisation rates and introduces severe distortions into atomic distributions for complex material systems.

We aim to develop continuum models and use this to derive a time-dependent mapping describing how ion initial positions on the sample surface correspond to final impact positions on the detector. When correctly calibrated with experiment, such a mapping could be used for performing reconstruction.

Currently we track the sample surface using a level set method, while the electric field is solved via BEM or a FEM-BEM coupling. These field calculations must remain accurate close to the boundary. Calibrating unknown evaporation parameters with experiment requires an ensemble of models per experiment. Therefore, we are also looking to maximise model efficiency via BEM compression methods i.e. fast multipole BEM. Efficiently constructing and reliably interpolating the non-bijective trajectory mapping, while accounting for ion trajectory overlap and instabilities (at sample surface corners), also presents intriguing problems.

This project is in collaboration with Cameca, the leading manufacturer of commercial atom probe instruments. If successful in minimising distortions such a technique could become valuable within the semiconductor industry.

In this talk we will show how the floating point errors in the simulation of SDEs (stochastic differential equations) can be modelled as stochastic. Furthermore, we will show how these errors can be corrected within a multilevel Monte Carlo approach which performs most calculations with low precision, but a few calculations with higher precision. The same procedure can also be used to correct for errors in converting from uniform random numbers to approximate Normal random numbers. Numerical results will be generated on both CPUs (using single/double precision) and GPUs (using half/single precision).

We begin by reviewing numerical methods for problems in one variable and find that univariate polynomials are the starting point for most of them.  A similar review in several variables, however, reveals that multivariate polynomials are not so important.  Why?  On the other hand in pure mathematics, the field of algebraic geometry is precisely the study of multivariate polynomials.  Why?

For a polynomial $h(x)$ in $F[x]$, where $F$ is any field, let $A$ be the
$F$-algebra given by generators $x$ and $y$ and relation $[y, x]=h$.
This family of algebras include the Weyl algebra, enveloping algebras of
$2$-dimensional Lie algebras, the Jordan plane and several other
interesting subalgebras of the Weyl algebra.

In a joint work in progress with Samuel Lopes, we computed the Hochschild
cohomology $HH^*(A)$ of $A$ and determined explicitly the Gerstenhaber
structure of $HH^*(A)$, as a Lie module over the Lie algebra $HH^1(A)$.
In case $F$ has characteristic $0$, this study has revealed that $HH^*(A)$
has finite length as a Lie module over $HH^1(A)$ with pairwise
non-isomorphic composition factors and the latter can be naturally
extended into irreducible representations of the Virasoro algebra.
Moreover, the whole action can be understood in terms of the partition
formed by the multiplicities of the irreducible factors of the polynomial
$h$.