Computational Fluid Dynamics (CFD) is crucial in fields like aerospace, 
biomedical engineering, and environmental modeling. Traditional CFD methods, 
reliant on solving the Navier-Stokes equations, are computationally intensive, 
requiring significant time and resources. Recent advancements in machine 
learning, especially Large Language Models (LLMs), offer the potential to reduce 
these computational demands. However, fluid dynamics' highdimensional, complex 
nature poses significant challenges for direct application of these models. The 
project will use the pattern recognition and reasoning abilities of pre-trained 
LLMs, enhanced with spatiotemporal-aware encoding, to predict unsteady fluid 
dynamics. By integrating LLMs with advanced spatiotemporal encoders, the aim is 
to bridge the gap between traditional CFD methods and modern machine learning 
approaches, improving accuracy and reducing computational costs. Computationally 
the process begins by breaking the domain down into smaller patches; LLMs such 
as LLaMA3 can be used to predict the future flow states based on the history of 
previous states. This is particularly effective in capturing the unsteady nature 
of fluid flows, where current states depend on prior conditions. The LLM's 
output is decoded into a grid that represents the fluid domain and refined using 
a Graph Neural Network (GNN), which propagates information across the grid, 
allowing for accurate prediction of the next state in the simulation. The 
results will be rigorously evaluated using standard CFD datasets, such as the 
Cylinder and Airfoil datasets, to assess its performance against existing 
machine learning methods. The model’s performance will be measured using metrics 
like Root Mean Squared Error (RMSE) across various prediction horizons. Overall 
this represents a novel approach to computational fluid dynamics, combining the 
strengths of LLMs with spatiotemporal-aware encoders to address the challenges 
of unsteady fluid dynamics prediction. The approach promises to enhance CFD 
simulation accuracy and efficiency, potentially establishing itself as a leading 
tool in the field. The project will provide an opportunity for the student to 
work beyond the cutting edge of CFD and establish themselves in the forefront of 
the wave of new AI technology which is revolutionising the subject