Bachelor and Master Theses

Title: Digital twins and AI for IoT
Subject: Computer Science
Level: Basic
Description: Big-Data and Machine-Learning/AI applied on the digital twin concept enables a number of desirable properties. Machin Learning is able to learn from previous data and use this to better track the rea status of the corresponding entity in the real world and predict what will happen. Reasoning with scenario cases (case-based reasoning, a subfield in Machin learning) both on micro and macro level for entities/Digital Twins enables identification of different outcomes of a mission and taking/proposing counteraction to reduce the risk of an undesired outcome. One powerful property of AI is that it can handle situations that are too complex to simulate within reasonable time and does not require detailed models of all entities (often impossible to create) and also handle sparse data. The learning capability enables the system to improve the identification of possible outcomes of an operation and probability for the different outcomes occurring. The improved availability of computational resources enables also knowledge discovery where the system may discover new strategies and also week links enabling improved micro level strategies, planning and operation.
Company: SAAB GROUP, kontaktperson: Ella Olsson
Student: Anton Roslund
IDT supervisors:
Peter Funk, +46-21-103153
Examinator: Ning Xiong
Ning Xiong, +46-21-151716

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