My apologies for forgetting this thread, I should have posted about my work here in the first place!
In order to avoid spamming the thread, I will post the link again to my original description.
In a sentence, I’m currently using Deep Learning models (mainly image-to-image translation) to predict, in real time, urban environmnetal performance. I’m generating my own synthetic and real life data to train the models and I have already pretrained models that can predict certain aspects of performance for 3 locations in the US. Results are quite encouraging (you can see some example predictions below, left image is input, middle is simulated, right is predicted) and the next step is to move to actually complex studies like thermal comfort.
I’m only partially using fast.ai for this, the cycleGAN implementation, and I’m eagerly waiting for more generative models to be introduced by the community (I will do my best as well once I get some time).
If anyone is interested in this let me know. I’m more than happy to help with my domain knowledge (environmental design) to get introduced into this important topic.
Kind regards,
Theodore.
