InFoMM

The vast majority of the world's electricity is generated by converting thermal energy into electric energy by use of a steam turbine. Siemens are one of the worlds leading manufacturers of such
turbines, and aim to design theirs to be as efficient as possible. Using an internally built software, Siemens can estimate the efficiency which would result from a turbine design. In this presentation, we present the approaches that have been taken to improve turbine design using mathematical optimisation software. In particular, we focus on the failings of the approach currently taken, the obstacles in place which make solving this problem difficult, and the approach we intend to take to find a locally optimal solution.

The latest CPUs by Intel and ARM support vectorised operations, where a single set of instructions (e.g. add, multiple, bit shift, XOR, etc.) are performed in parallel for small batches of data. This can provide great performance improvements if each parallel instruction performs the same operation, but carries the risk of performance loss if each needs to perform different tasks (e.g. if else conditions). I will present the work I have done so far looking into how to recover the full performance of the hardware, and some of the challenges faced when trading off between ever larger parallel tasks, risks of tasks diverging, and how certain coding styles might be modified for memory bandwidth limited applications. Examples will be taken from finance and Monte Carlo applications, inspecting some standard maths library functions and possibly random number generation.

We consider the problem of localising non-negative point sources, namely finding their locations and amplitudes from noisy samples which consist of the convolution of the input signal with a known kernel (e.g. Gaussian). In contrast to the existing literature, which focuses on TV-norm minimisation, we analyse the feasibility problem. In the presence of noise, we show that the localised error is proportional to the level of noise and depends on the distance between each source and the closest samples. This is achieved using duality and considering the spectrum of the associated sampling matrix.

We explore the use of applying multiple preconditioners for solving linear systems arising in simulations of incompressible two-phase flow. In particular, we use a selective MPGMRES algorithm, for which the search space grows linearly throughout the iterative solver, and block preconditioners based on Schur complement approximations

We propose a new parameter estimation technique for SDEs, based on the inverse problem of finding a forward operator describing the evolution of temporal data. Nonlinear dynamical systems on a state-space can be lifted to linear dynamical systems on spaces of higher, often infinite, dimension. Recently, much work has gone into approximating these higher-dimensional systems with linear operators calculated from data, using what is called Dynamic Mode Decomposition (DMD). For SDEs, this linear system is given by a second-order differential operator, which we can quickly calculate and compare to the DMD operator.