Consider I have a geometry and some pressure distribution around it. For each case geometry is changing so does pressure distribution. I have solutions of pressure distribution for each geometry. What i want to do is to build an encoder decoder model which predicts pressure distribution for a new geometry when i want to. To do that i need two images in my dataset. One is showing geometry and the other one is showing pressure distribution around it which behaves ground truth.
As i said i have solutions of pressure for each geometry. I can plot them and generate images of them but i already have raw data for pressures which is in the shape of [n, n]. I can also have geometry data [n, n]. So i thought that i do not need to generate images if i have this raw data.
I am saying again i am new in pytorch:) If you have any suggestion about my study or tutorial or any other document, please share with me.