Robust and Scalable Methods for Large-Scale Networked Systems under Severe Uncertainty

Abstract

Large-scale networked systems are increasingly used to represent critical infrastructures, energy systems, transportation networks, supply chains, and digital twins. Their growing interconnectedness introduces complex dependencies and cascading effects, while the available information is often incomplete, imprecise, or deeply uncertain. In such settings, classical probabilistic methods frequently require assumptions that are difficult to justify and may lead to overconfident predictions and unreliable decisions.

This lecture reviews recent developments in robust and scalable computational methods for analysing large-scale networked systems under severe uncertainty. The focus is on methodologies that explicitly represent incomplete knowledge using interval methods, probability boxes, credal sets, and other imprecise probability frameworks, and on efficient algorithms capable of propagating these uncertainties through complex networks. Particular emphasis is placed on scalable inference methods, including advanced simulation techniques, surrogate modelling, survival signatures, and message-passing algorithms such as the Information Propagation Algorithm, enabling near real-time assessment of large interconnected systems.

The lecture argues that the future of reliability computations lies not in producing increasingly precise predictions, but in developing methods that remain trustworthy when knowledge is fundamentally incomplete and systems continue to grow in scale and complexity.

Bio

Edoardo Patelli is Professor of Risk and Uncertainty and Head of the Centre for Intelligent Infrastructure at the University of Strathclyde, UK. He is also Director of Research in the Department of Civil and Environmental Engineering and Scientific Director of the recently awarded £23M STAND-UP doctoral programme in nuclear engineering and advanced manufacturing. Previously, he served as Deputy Director of the Institute for Risk and Uncertainty and Co-Director of the EPSRC-ESRC Centre for Doctoral Training in Risk and Uncertainty at the University of Liverpool.

His research focuses on uncertainty quantification, imprecise probabilities, reliability analysis, and scalable computational methods for complex systems and networked infrastructures. He has developed methodologies and software tools for robust uncertainty propagation, advanced simulation, network reliability, and decision making under severe uncertainty, with applications in nuclear safety, resilient infrastructure, digital twins, aerospace systems, and human reliability analysis.

Professor Patelli has authored more than 300 scientific publications, including a recent book on engineering uncertainty, and has accumulated more than 6,800 citations with an h-index of 42. He serves on the Executive Board of the International Association for Reliability Engineering and Risk Management (IARERM), is an Advisory Board member of ClimateXchange, and is a member of the Bernoulli Society Committee on Probability and Statistics in the Physical Sciences.