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Abschlussarbeiten, Bachelor- und Masterarbeiten
Sie suchen gerade eine Diplomarbeit, ein Thema für eine Bachelor oder Master Thesis? Dann sind Sie hier richtig. In diesem Bereich sind Abschlussarbeiten aus allen Fakultäten zu finden.
Beachten Sie auch den entsprechenden Stichwortindex.
Wenn Sie selbst eine Diplomarbeit ausschreiben wollen, lesen Sie bitte vorher unbedingt das 'Best Practice Manual Stellenanzeigen'.
20.06.2024
3 M.Sc thesis topics in collaboration with Carl Zeiss AG at the interface of Machine Learning and Physical Modeling
Kontakt: p.s.koutsourelakis@tum.de
20.06.2024
M.Sc. thesis on the topic of "Inverse Materials Design"
Kontakt: yaohua.zang@tum.de
04.06.2024
Bachelor's thesis and Master's thesis in the field of all-solid-state batteries
We are looking for highly motivated undergraduate and graduate students in chemistry and physics who want to write their thesis in the field of solid-state batteries. Your task will be to fabricate, characterize and test solid electrolyte membranes and to develop appropriate equivalent circuit models to describe their impedance behavior.
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Kontakt: susana.suttor@tum.de
16.04.2024
Scientific machine learning through physics-informed neural networks
We are seeking a highly motivated student to develop a novel framework for Physics-Informed Neural Networks (PINNs) that overcomes current limitations.
Basics of Physics-Informed Neural Networks (PINNs):
PINNs are a powerful machine learning technique that combines the strengths of neural networks and physics. Here's a breakdown:
Neural Networks: These are algorithms inspired by the human brain, capable of learning complex patterns from data.
Physics: Scientific principles governing the behavior of matter and energy.
PINNs leverage the data-driven learning power of neural networks while incorporating physical laws through governing equations (often described by Partial Differential Equations - PDEs). This allows PINNs to:
Learn from data: Analyze existing observations or measurements of a physical system.
Enforce physical laws: Ensure the learned model adheres to established physical principles.
Handle complex systems: Model intricate physical phenomena that might be difficult to solve with traditional methods.
Project Focus:
This project builds upon the foundation of PINNs and aims to develop PINNs model that can model 2 Dynamic Systems:
Spring Mass Damper System
Inverted pendulum
Furthermore, the models should be:
Independent of initial conditions: Produces accurate results regardless of the system's starting state.
Partially independent of external forces: While the type of force needs to be known, the model should be able to infer the force equation from data.
Independent of natural frequency: Applicable to various systems with different inherent oscillation frequencies.
Generalizable: Analyze the effectiveness of incorporating advanced neural network architectures like Recurrent Neural Networks (RNNs) to increase generalizability.
This thesis will, therefore, focus on the combination of data-driven ML model and Physics behind the dynamic systems to gain the benefits of both worlds. It would be part of the project to evaluate if PINNs trained on simulated data can be extended to real systems. Furthermore, it would be part of the project to evaluate the impact of known physical model, unknown physical model, known inputs to the real physical system, unknown inputs to the real physical system etc. and their pros and cons.
Project Benefits:
Opportunity to work on cutting-edge research at the intersection of physics and machine learning.
Hands-on experience in developing and implementing advanced neural network models.
Develop strong technical skills in machine learning and scientific computing.
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Kontakt: tanmay.goyal@tum.de
12.03.2024
Spatiotemporal Interpolation and Fusion of High-Resolution Satellite Data for Urban Area
Kontakt: junwei.li@tum.de
11.09.2023
Master / Bachelor Thesis: Control Strategy for Energy Management Systems for Commercial electrical Energy Systems
In this thesis, it is investigated how an energy management system should be structured so that it can serve different use cases simultaneously or consecutively.
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Kontakt: a.hirnet@sonnen.de
19.10.2021
Embedded System für Robot Vision Anwendungen (BA/MA)
In dieser Arbeit soll ein Embedded Vision System für Robotikanwendungen aufgebaut werden. Dabei kommt eine kompakte und leistungsfähige 3D Kamera sowie ein Embedded Board mit GPU zum Einsatz. Nach Aufbau und Einarbeitung in das System sollen Vision-Algorithmen für die GPU angepasst, entwickelt und auf dem Board implementiert werden. Dabei geht es vorrangig um Verfahren zum Tracking und zur Lageschätzung (6D Pose estimation).
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Kontakt: jobs@visevi.com
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