Uncertainty Quantification for Complex Systems – Development in Theory and Methodologies

Uncertainty quantification (UQ) is a modern inter-disciplinary science that cuts across traditional research groups and combines statistics, numerical analysis and computational applied mathematics. UQ methodologies are useful for taking account of uncertainties when mathematical and computer models are used to describe real-world phenomena. This helps to better inform decisions, assess risk and formulate policies across multiple areas as diverse as climate modelling, manufacturing, energy, life sciences, finance, geosciences and more.

The scientific challenges of modern life, along with the recent rapid growth in computing power and the demand for more accurate and precise predictions in areas affecting improved infrastructures, public safety and economic well-being have spawned a recent surge in UQ activity. New UQ methodologies have and are continuing to be developed by statisticians and applied mathematicians independently.

The workshop is part of the six month programme at the INI on Uncertainty Quantification for Complex Systems: Theory and Methodologies and will take place towards the end of the Programme, so will focus on disseminating the key outputs and will highlight some potential outcomes that could be taken forward. It follows an earlier event within the Programme that looked at the challenges faced by some specific problem holders.

Aims and Objectives

This knowledge exchange event by the Turing Gateway to Mathematics will feature a number of talks from academia as well as end users. It will provide the opportunity for those from industry and the public sector, to access state-of-the-art theory and methods, as well as learn about best practice and help to foster links between the various communities. It will help to further consolidate opportunities for collaboration between statisticians and applied mathematicians

A short introductory talk will provide an overview of the Uncertainty Quantification Research Programme. This will be followed by a number of academic talks that will review progress made over the duration of the Programme, in relation to some of the key research themes including:

Surrogate Modelling Multi-level and Multi-fidelity Methods Dimension Reduction Strategies Inverse Problems Design

Two end-user sessions will include talks from the environmental/climate and energy infrastructure sectors. Speakers will describe how uncertainty is managed at present in their organisations and the challenges they face.

This event will be of relevance to individuals from multiple sectors including energy infrastructure, engineering, environmental modelling, manufacturing, Government and the public sector.