First Running! Deep learning to CFD

This is our first apply ConvLSTM to CFD successfully! although the case is simple and under control of lots of factors. The ground factor is generated by Openfoam, and the custom model is predNet from coxlab. We trained three models in this time.

1.Training: use Nth frame to predict (N+1)th frame
Prediction: use 1-10th frame to predict 2-11th frame, then combined 11th frame in the predicted output with 1-10th frame. With new input(2-10th are ground truth, 11th is predicted), we can keep predicting 3-12th frame. In this experience, we predict the frames until 20th where sliding window = 1 frame. ( only first few frame are good, since we use the predicting frames to do the prediction)


2.Training: Nth frame to predict (N+10)th frame
Prediction: use 1-10 frame to predict 11-20 frame, no sliding window. ( this is very good since all input are ground truth)


3.Training: use Nth frame to predict (N+1)th frame
Prediction: use one frame to predict next frame, like driving prediction (animation has a little problem. right side is prediction, left side is ground truth)


Do we really know how water moves?

Close your eyes, think about what would happen when the spray beats the shore, or the water from faucet comes into your body.
This is a simplest animation showing how water changes its speed after it counters a wall.


The color is speed of water and the water is from left side with 1m/s. Can anyone has ability to simulate it in our brain? I guess its super hard unless you see thousands of similar pictures like this.
So that if the simulation becomes much complex than this one like I mentioned before the spray beats the shore, I guess there will be a big gap between our imaginations and real situations.

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