As we accelerate into the 21st century, our backbone engineering systems are becoming increasingly complex and connected. This leads to critical infrastructure systems comprised of interdependent sub-systems, each governed by complex non-linear dynamics and cascade interactions. To address issues of interconnected resilience and prevent cascade failures, it is necessary to ensure we understand the nature of these challenges, which requires collaboration between industrial practitioners and academics using an interdisciplinary approach.  

This session engaged stakeholders covering energy, telecommunications, transport, and water infrastructures and focused on recent advances in machine learning and complexity science, and how these methods can give us greater understanding of interconnected complexities in infrastructure as well as providing early warning capabilities. This was a joint workshop sponsored by the Data-Centric Engineering programme at the Alan Turing Institute, funded by the Lloyd’s Register Foundation.

Agenda:

10:30 Arrive – coffee/tea
11:00 Welcome – Liz Varga & Weisi Guo
11:05 Key note – Dr. Simon Thompson (Head of Big Data & Customer Experience Practice at BT)
11:40 Machine Learning for Infrastructure Monitoring – Dr. David Green (Research Fellow at Turing)

12:10 Panel discussion - Simon, David, Liz, Weisi with 5 minute thought pieces from each followed by audience Q&A
13:00 Lunch and networking
14:00 Break out tables (5 x 6) (coffee available)

The breakout themes include: 1) best practices for data sharing, 2) recent advances in machine learning, 3) critical needs by industry.
15:15 Plenary feedback from tables
15:45 Discussion and next steps
16:00 Close

 

Data-Centric Engineering programme at the Alan Turing Institute, funded by the Lloyd’s Register Foundation.

Share the NEWS