ESI symposium 2019
Intelligence, the next challenge in system complexity?
High-tech systems are integrating more and more intelligence, which increases engineering complexity. At the 2019 ESI Symposium we will discuss the challenges this presents and initial directions for managing, and coping with, the growing complexity.
We would like to invite you to join the symposium. The event features a varied program with interactive sessions inspired by contributions from industrial and academic speakers. Moreover, there will be plenty of networking opportunities on the innovation market. Please register now.
We look forward to seeing you.
Wouter Leibbrandt and Frans Beenker
Keynote speaker from industry: Henk van Houten, CTO and Head of Research for Royal Philips
Keynote speaker academy: Edward A. Lee, Professor in Electrical Engineering and Computer Sciences, University of California, Berkeley
- Digital Twinning
How can digital twinning drive design and engineering innovation?
A digital twin is a virtual representation of a physical entity or system. A digital twin is much more than a picture, blueprint or schematic: It is a dynamic, simulated view of a physical product that is continuously updated throughout the design, build and operation lifecycle, and exists in parallel to its corresponding physical object. Digital twins can provide customer and equipment insights, improve quality and reliability, monitor performance, and mitigate downtime and increase availability. This session looks at the potential of digital twins to drive innovation, ways to introducing digital twinning as a way-of-working in the high tech industry, and its impact on an R&D organization.
- Learning systems
How to exploit system data for operational excellence?
Emerging operational data, a direct consequence of systems increasingly being equipped with sensors and software, could help industry to deal with the key challenges in industry’s shift towards complex digitised, connected, intelligent solutions. How to make these systems resilient to change, capable to autonomously self-adapt their operations upon unforeseen conditions? Will augmenting systems with introspective and machine-learning capabilities be the next step in supporting evolvability? What is needed to make self-learning systems genuine artificial intelligent? These and many other questions are being discussed in this session, with a focus on pursuing operational excellence by means of self-learning behavior.
- Architecting intelligent systems
Is a paradigm shift needed in architecting intelligent systems?
The examples of artificial intelligent systems, such as self-driving cars, autonomous drones or autonomous weapons, are all well-known, hyped may be. From architecting perspective, does it make a difference to call them artificial intelligent and does that influence the way architects have to do their job? Is a paradigm shift needed to architect these systems? Can architects delegate their work to smart digital assistants? These and many other questions are being discussed in this session, with a focus on the impact of AI on systems architecting.
- Diagnostic reasoning
Model based reasoning to support diagnostics in complex systems
As systems become increasingly complex, diagnosing system failures and performance issues becomes a true challenge for engineers. Too often, solving problems requires extensive involvement of multiple R&D experts. Diagnostic reasoning gives engineers the tools they need to make decisions about system behaviour and performance without having to know all the intrinsic system details. In addition to presenting a state-of-the-art overview and a look into the future, this session presents industrial cases in which data, modelling, and reasoning are brought together to solve diagnostic challenges.
- Acceptable AI
How to obtain trust in systems with AI components?
Next to the technical complexity of integrating AI into high-tech systems, we have to address the complex task of making the resulting system acceptable by its end-users. This track discusses the possible routes and roadblocks of getting AI explained and accepted. The presentations are based on industrial use cases, including healthcare applications and automotive applications.
- Future system engineers
What does future systems engineering look like? Will the rise of intelligent systems threaten or augment SE practices?
The introduction of AI techniques promises to automate many engineering activities, while at the same time it further increases the complexity of systems. This requires an increasing emphasis on SE competencies such as systems thinking, holistic lifecycle view, etc. How does this impact systems engineering organizations, and SE competency models? How can industry prepare, and what are universities doing in this area?
More information follows.