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:
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:
Project Focus:
This project builds upon the foundation of PINNs and aims to develop PINNs model that can model 2 Dynamic Systems:
Furthermore, the models should be:
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:
Desired Skills:
Background in machine learning. Basic understanding of physics concepts behind Spring Mass Damper System and Inverted Pendulum. Experience with ML libraries (e.g., Python, TensorFlow, PyTorch). Basic knowledge of Simulink would be beneficial.
Note:
The supervision would be english. We can work with language issues but basic English understanding would be needed.