Applied AI Lunch Talk
Physical Informed Neural Network for hybrid condition monitoring
At FORCE Technology we are investigating how to improve hybrid testing, by using machine learning as an approach to address the complex challenges in condition monitoring. We are integrating advanced neural network techniques with an understanding of the underlying physical principles, this cutting-edge methodology offers a powerful solution to efficiently analyze and predict the behavior of assets.
Introducing the new AI Test and Experimentation facility in DK
EU is investing heavily in addressing the gap between European and international capabilities in AI. One aspect is the ability to test AI before entry to market and DTU is coordinating the one for Smart Cities and Communities AI and robotics domain, known as CitCom.ai. The purpose of CitCom is to accelerate new AI and Robotics innovations to the market and will offer support, especially for SMEs and their cutting-edge solutions, and provide tests that can serve to get the first difficult customer. This talk is for SMEs, investors, lab responsibles, and buyers of AI and Robotics as well as others with an interest in how tests can provide trust, validation, and market potential of specific solutions.
Mikael Fuglsang, Team Lead for Robotics and Project Manager Digital Services at Force Technologies
Changing the way our customers work with data from their quality inspection. Working in the field of building automated mechanics solutions tailored to fit their product inspection and training AI models to become specialized in analyzing the data for quality control.
Rasmus Sune Reeh, Program Leader at the European Testing and Experimentation Facility (TEF)
Rasmus Reeh is the project lead of CitCom.ai and deputy at Connecting Communities at DTU Compute Dynsys. An economist by training, he has worked more than 10 years within different roles in the Smart City domain, including DTU Elektro and most recently as Development Lead of Copenhagen Solutions Lab.
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