Simulation of Fluid Dynamics Using Physics-Inspired Deep Learning
BackIn this project, I applied advanced deep learning techniques inspired by physics to simulate the fluid dynamics around a rotating object. The fluid in this simulation is incompressible, meaning it retains a constant density throughout the process. By leveraging deep learning, specifically physics-informed neural networks (PINNs), I was able to model the complex interactions that govern fluid behavior around rotating bodies. This approach aids in efficiently solving challenging mathematical problems, such as partial differential equations (PDEs), which are crucial for accurately capturing fluid motion.
To carry out the simulation, I utilized PhiFlow, an open-source simulation toolkit designed for machine learning and fluid dynamics research. This powerful platform enabled me to incorporate physics-informed models seamlessly into the simulation process, allowing for both accuracy and computational efficiency.
The integration of AI and machine learning with fluid mechanics opens new avenues for tackling difficult problems in computational science. It not only improves the efficiency of simulations but also enables solving intricate systems that would be otherwise computationally prohibitive.