Data-Physics Dual-Driven (Hybrid PINN) Modelling for Next-Generation Aerospace Structural Dynamics

About the Project

Are you passionate about the fusion of Artificial Intelligence and structural dynamics? Do you want to contribute to cutting-edge research that will transform aircraft structural health monitoring and real-time digital twins?

Applications are invited for a 3.5 to 4-year PhD position focusing on "Data-Physics Dual-Driven Modelling for Next-Generation Aerospace Structural Dynamics" in the research group of Dr Jun Wu at the University of Birmingham.

Conventional structural dynamics relies heavily on high-fidelity Finite Element Analysis (FEA), which is computationally prohibitive for real-time applications. On the other hand, pure data-driven AI models lack physical constraints and generalizability. This PhD project aims to bridge this gap by developing a novel Hybrid Physics-Informed Neural Network (Hybrid PINN) framework. The framework will integrate sparse, noisy experimental sensor data with governing partial differential equations (PDEs) of structural dynamics to achieve real-time, full-field dynamic response reconstruction and material parameter identification for complex aircraft components.

Project details:

The successful candidate will adopt an integrated computational–experimental approach, spanning from theoretical algorithm development to laboratory validation. The research will systematically progress through four clear phases, scaling up in structural complexity:

Phase 1: Single Structural Component

You will start with foundational theory, training the PINN to solve classic vibration equations for a single isolated component (e.g., a beam or a plate) to accurately map its dynamic behaviors.

Phase 2: Multi-Structure Assembly & Coupling

You will advance the framework to handle multi-structure coupling. You will develop "interface boundaries" in the neural network to seamlessly connect different components. The network must learn to balance force and displacement transmissions across these coupled junctions.

Phase 3: Geometry Complexity & Real-World Non-linearities

You will scale the model to realistic aircraft assemblies featuring geometric complexities and physical non-linearities.

Phase 4: Laboratory Validation & Real-Time Deployment

You will conduct hands-on laboratory testing on a physical, multi-component assembled wing prototype. This noisy, real-world experimental data will be used to calibrate your coupled PINN model, which will finally be deployed onto edge-computing hardware for millisecond-level digital twin monitoring.
This project directly aligns with the future of digital aviation, smart structures, and digital twin technology. You will join a vibrant, multidisciplinary research team with access to state-of-the-art computational clusters and dedicated experimental dynamics facilities.

Funding and Financial Information
Please note that this specific position is self-funded or intended for applicants who secure their own external funding. However, the research group will fully and actively support outstanding candidates applying for external scholarships. Additional internal group funding will be available to cover research consumables, high-performance computing resources, and attendance at international academic conferences and trainings.

Candidate requirements:

Essential:
• A 1st or 2:1 class (or equivalent) undergraduate or Master’s degree in Aerospace Engineering, Mechanical Engineering, Applied Mathematics, Data Science, or a related discipline.
• Strong motivation for both mathematical algorithm development and hands-on laboratory testing.
• Good written and oral communication skills in English.

Desirable:
• Theoretical background in Structural Dynamics or Finite Element Method (FEM).
• Proficiency in programming languages, specifically Python and deep learning frameworks (PyTorch or TensorFlow).
• Prior experience with Physics-Informed Machine Learning (e.g., PINN).

How to apply:
Interested candidates are highly encouraged to make an informal application with a curriculum vitae (CV), a brief cover letter summarizing your research interests and suitability for the position, contact details of two referees, and academic transcripts by emailing Dr Jun Wu ([email protected]), before submitting a formal application. Formal applications will subsequently be made through the university’s online application system (https://sits.bham.ac.uk/lpages/EPS024.htm).

Funding notes:
Please note that this specific position is self-funded or intended for applicants who secure their own external funding. However, the research group will fully and actively support outstanding candidates applying for external scholarships. Additional internal group funding will be available to cover research consumables, high-performance computing resources, and attendance at international academic conferences and trainings.