# Selected Presentations

##### M. Croci and M. B. Giles, Solving parabolic PDEs in half precision, SIAM CSE 2021 and NLA Group meeting, University of Manchester (2020-2021).Download.

Motivated by the advent of machine learning, the last few years saw the return of hardware-supported low-precision computing. Computations with fewer digits are faster and more memory and energy efficient, but can be extremely susceptible to rounding errors. An application that can largely benefit from the advantages of low-precision computing is the numerical solution of partial differential equations (PDEs), but a careful implementation and rounding error analysis are required to ensure that sensible results can still be obtained.

In this talk we study the accumulation of rounding errors in the solution of the heat equation, a proxy for parabolic PDEs, via Runge-Kutta finite difference methods using round-to-nearest (RtN) and stochastic rounding (SR). We demonstrate how to implement the numerical scheme to reduce rounding errors and we present \emph{a priori} estimates for local and global rounding errors. Let $u$ be the roundoff unit. While the worst-case local errors are $O(u)$ with respect to the discretization parameters, the RtN and SR error behaviour is substantially different. We show that the RtN solution is discretization, initial condition and precision dependent, and always stagnates for small enough $\Delta t$. Until stagnation, the global error grows like $O(u\Delta t^{-1})$. In contrast, the leading order errors introduced by SR are zero-mean, independent in space and mean-independent in time, making SR resilient to stagnation and rounding error accumulation. In fact, we prove that for SR the global rounding errors are only $O(u\Delta t^{-14})$ in 1D and are essentially bounded (up to logarithmic factors) in higher dimensions.

##### M. Croci, M. B. Giles, P. E. Farrell, M. E. Rognes, V. Vinje, Non-nested multilevel Monte Carlo methods with applications to brain simulation, Oden Institute (2021).Download.

This talk consists of two parts. In the first, we develop two new strategies for spatial white noise and Gaussian-Matérn field sampling that work within a non-nested multilevel (quasi) Monte Carlo (ML(Q)MC) hierarchy. In the second, we apply the techniques developed to quantify the level of uncertainty in a new stochastic model for tracer transport in the brain.

The new sampling techniques are based on the stochastic partial differential equation (SPDE) approach, which recasts the sampling problem as the solution of an elliptic equation driven by spatial white noise. The efficient sampling of white noise realisations can be computationally expensive. In this talk, we present two new sampling techniques that can be used to efficiently compute white noise samples in a FEM-MLMC and FEM-MLQMC setting. The key idea is to exploit the finite element matrix assembly procedure and factorise each local mass matrix independently, hence avoiding the factorisation of a large matrix. In a multilevel framework, the white noise samples must be coupled between subsequent levels. We show how our technique can be used to enforce this coupling even in the case of non-nested mesh hierarchies.

In the MLQMC case, the QMC integrand variables must also be ordered in order of decaying importance to achieve fast convergence with respect to the number of samples. We express white noise as a Haar wavelet series whose hierarchical structure naturally exposes the leading order dimensions. We split this series in two terms which we sample via a hybrid standard Monte Carlo/QMC approach.

In the final part of the talk, we employ a combination of the methods presented to solve a PDE with random coefficients describing tracer transport within the interstitial fluid of the brain.

##### M. Croci, M. B. Giles, M. E. Rognes, and P. E. Farrell, MLQMC Methods for Elliptic PDEs Driven by White Noise, SIAM CSE 2019, Spokane, USA and ICIAM 2019, Valencia, Spain.Download.

When solving partial differential equations driven by additive spatial white noise, the efficient sampling of white noise realizations can be challenging. In this paper we focus on the efficient sampling of white noise using quasi-random points in a finite element method and multilevel Quasi Monte Carlo (MLQMC) setting. This work is an extension of previous research on white noise sampling for MLMC.

We express white noise as a wavelet series expansion that we divide in two parts. The first part is sampled using quasi-random points and contains a finite number of terms in order of decaying importance to ensure good QMC convergence. The second part is a correction term which is sampled using standard pseudo-random numbers.

We show how the sampling of both terms can be performed in linear time and memory complexity in the number of mesh cells via a supermesh construction. Furthermore, our technique can be used to enforce the MLQMC coupling even in the case of non-nested mesh hierarchies. We demonstrate the efficacy of our method with numerical experiments.

##### M. Croci, M. B. Giles, M. E. Rognes, and P. E. Farrell, Efficient white noise sampling and coupling for multilevel Monte Carlo, MCQMC 2018, Rennes, France.Download.

When solving stochastic partial differential equations (SPDEs) driven by additive spatial white noise the efficient sampling of white noise realizations can be challenging. In this talk we present a novel sampling technique that can be used to efficiently compute white noise samples in a finite element and multilevel Monte Carlo (MLMC) setting.

After discretization, the action of white noise on a test function yields a Gaussian vector with the FEM mass matrix as covariance. Sampling such a vector requires an expensive Cholesky factorization and for this reason P0 representations, for which the mass matrix is diagonal, are generally preferred in the literature. This however has other disadvantages. In this talk we introduce an alternative factorization that is naturally parallelizable and has linear cost and memory complexity (in the number of mesh elements).

Moreover, in a MLMC framework the white noise samples must be coupled between subsequent levels so as to respect the telescoping sum. We show how our technique can be used to enforce this coupling even in the case in which the hierarchy is non-nested via a supermesh construction. We conclude the talk with numerical experiments that demonstrate the efficacy of our method. We observe optimal convergence rates for the finite element solution of the elliptic SPDEs of interest. In a MLMC setting, a good coupling is enforced and the telescoping sum is respected.