WELCOME
to the AI Summit Brainport 2025,
which will take place on Thursday the 13th of November, at the Evoluon Eindhoven so save the date!
We invite you to explore this year’s theme: “How to trust AI? The Good, the Bad and the AI.”
The summit will tackle the frontier of artificial intelligence trustworthiness.
Join leading experts and innovators as we delve into the promise and peril of AI: from explainable AI and verifiable models that lift the veil on the “black box”, to the ethical implications, bias, and fairness that shape AI’s impact on society.
We will address existential risks posed by advanced AI, cutting-edge strategies for AI security, and practical ways to ensure safe deployment of AI models. Who bears responsibility when AI goes wrong, and how can we make accountability actionable?
Through interactive tracks and dynamic sessions, discover how to harness the good, confront the bad, and shape the future of trustworthy AI.
This year's summit introduces two exciting new tracks: Understanding AI and AI Implementations, replacing the Expert and Adoption tracks. Additionally, the EAISI Research track will return, along with the AI Pitch Competition Final.
Contrary to previous years the summit will start at 9.45h but the doors will open at 8.45h so you can have a coffee and do some networking.
All track partners are currently putting their programs together so watch this space, we will open registration mid June!
PRELIMINARY MORNING PROGRAM
|
09:00 |
Walk in with tea/coffee, Expo |
PRELIMINARY AFTERNOON PROGRAM
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13:00 |
Lunch break & Expo |
Carlo van de Weijer
Carlo will lead the plenary session of the program.
View profile on TU/e website
Martijn van Gruijthuijsen
Delegate Province of North Brabant
Portfolio Economy, Talent Development and Finance
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Carlo van de Weijer
General Manager EAISI TU/e and Chair Brainport AI Hub
Carlo will lead the plenary session of the program.
View profile on TU/e website
Nathan van de Wouw
Full Professor at Eindhoven University of Technology
Synergy of Models and Data: AI Innovations in Engineering Diagnostics and Control
This talk will explore the value and potential of harmonizing physics-based knowledge with data and machine learning techniques to develop hybrid models with superior predictive capabilities. These hybrid models are critical for enhancing diagnostics capabilities, such as fault detection, isolation, and root cause analysis, thereby supporting predictive maintenance.
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Abstract
The design and operation of complex engineering systems demand reliable models that accurately describe their dynamic behavior, forming the foundation of model-based engineering. With the surge in data availability and advancements in machine learning, the integration of these technologies into model-based engineering presents both significant opportunities and challenges.
Hybrid models are critical for enhancing diagnostics capabilities, such as fault detection, isolation, and root cause analysis, thereby supporting predictive maintenance.
Moreover, controllers that shape the dynamic behavior of engineering systems in terms of performance and robustness also stand to benefit from these hybrid technologies.
We will illustrate the transformative impact of combining models, data, and learning on various applications, including high-tech equipment, healthcare, and mobility. Examples will span from robots and semiconductor equipment to mechanical ventilators in hospitals and autonomous vehicles. Additionally, we will address the challenges encountered in this integration and share valuable lessons learned from practical implementations.
Martijn van Gruijthuijsen
Delegate Province of North Brabant
Portfolio Economy, Talent Development and Finance
View his profile
Jeroen Dijsselbloem
Mayor of Eindhoven
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Photographer: Jiri Büller
Organizing partners
