Computational Engineering
PhD Studentship: Development of Innovative and Efficient Computational Fluid Dynamics Simulator based on Physics-Informed Neural Networks
Manchester Metropolitan University
Despite significant advances in numerical techniques and computing hardware, the high computational cost of large-scale 3D computational fluid dynamics (CFD) modelling remains a major challenge. A new approach has emerged that integrates data and mathematical models through neural networks. This has led to the development of a method for solving partial differential equations (PDEs) known as physics-informed neural networks (PINNs).
- Location
- Manchester, United Kingdom
- Deadline
- 4 October 2026
- Funding
- UK Students; £31,236