Coupling Neural Network with a CFD Solver
In this video, I demonstrate the process of training a physics informed neural network and implementing it in a CFD solver to create a custom boundary condition. Below you can find the link to the github repository:
Github: github.com/ComputationalDomai...
Resources:
- doi.org/10.1115/ICEF2022-90371
- doi.org/10.1016/j.energy.2021...
- doi.org/10.2514/1.J059997
- www.tfd.chalmers.se/~hani/kur...
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Пікірлер: 7
Nice start. Thank you for sharing!
Thanks for sharing. Notice you model the flow around cylinder that is in the wake of another cylinder. Where the two cylinders of the same diameter? When you model step cylinders , you can get interesting results such as karman vortex shedding and other phenomena. It would defnitely be of interest if you were to apply PINNs to the CFD of other geometric shapes.
amazing work!!!!! :)
Parabéns, lindo
Excellent work! I've been delving into a similar area, albeit focused on the individual particle level. I've successfully trained a neural network to calculate particle dynamics in a single step, effectively replacing the need for 25 traditional computational substeps of Verlet integration. Interestingly, the network yields significantly more stable results compared to numeric integration when subjected to high-energy collisions. Unlike Verlet integration, the network simulation does not "explode". Have you experimented with your simulation at energy levels exceeding those used during training?
عندما كنت لا أزال في المدرسة الابتدائية ، كان هناك Pawe كنت أركب دراجة التقيت به ثم ذهبت إلى الخنفساء للحصول على الآيس كريم ، وفي طريقي إلى المنزل عدت إلى المنزل