Events Calendar
Welcome to your calendar for data science events! Dive into a curated list of courses, conferences, seminars, workshops, and key deadlines. Tailor your search to match your interests by adjusting the event category filters. For those specifically looking for PhD courses, simply modify the filter settings to include these events as well. Stay connected and up-to-date with the latest in data science, all in one place.
- This event has passed.
Modelling, Verification, Performance Analysis, Refinement, Synthesis and Machine Learning for Cyber Physical Systems (2024)
December 2 - December 5
Welcome to Modelling, Verification, Performance Analysis, Refinement, Synthesis and Machine Learning for Cyber Physical Systems
Organizer: Kim Guldstrand Larsen
Lecturers: Kim Guldstrand Larsen, Marius Mikucionis, Peter Gjøl Jensen, Ulrik Nyman
ECTS: 3
Date/Time: 2, 3, 4, 5, 6 December 2024
Deadline: 11 November 2024
Max no. Of participants: 30
Description: Timed automata and games, priced timed automata and energy automata have emerged as useful formalisms for modeling real-time and energy-aware systems as found in several embedded and cyber-physical systems. During the last 20 years the real-time model checker UPPAAL has been developed allowing for efficient verification of hard timing constraints of timed automata. Moreover a number of significant branches exists, e.g. UPPAAL CORA providing efficient support for optimization, and UPPAAL TIGA allowing for automatic synthesis of strategies for given safety and liveness objectives, and ECDAR supports refinement and compositional development of real-time systems. Also the branch UPPAAL SMC, provides a highly scalable new engine supporting (distributed) statistical model checking of stochastic hybrid automata, and most recently the new branch UPPAAL STRATEGO supporting safe and optimal strategies for stochastic hybrid games by combining symbolic methods with machine learning. The course will review the various branches of UPPAAL, the corresponding modeling formalisms as well as the symbolic or statistical algorithms applied. Also, examples on applications of the tools suite will be given to a range of real-time and cyber-physical examples including schedulability and performance evaluation of mixed criticality systems, modeling and analysis of biological systems, energy-aware wireless sensor networks, smart grids and smart houses and battery scheduling.
Prerequisites: A general background in computer science or a related engineering discipline with basic knowledge about finite-state automata, probability theory and ordinary differential equations. Some basic programming skills are needed.
Disclaimer:
DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
