I am attempting to reproduce parts of a dynamic climate model using machine learning in place of physics code.
I have both inputs and outputs for several climate model runs, and my intention is to use these to train a machine learning model, hopefully to the point where it can predict similar output values when given the same inputs.
The nature of the input/output (feature/label) datasets is that they have a 4-D structure per feature/label, with dimensions (time, elevation, lat, lon).
Initially, I am assuming that only adjacent lat/lon cells should influence the behavior of neighboring cells, but later we may want to incorporate algorithms that also take into account adjacent cells in the elevation dimension, as well as something to account for the temporal nature of the data (i.e. we have a time series for each lat/lon/elevation).
Can anyone comment as to a good first approach using the fastai library? My initial idea was to use one or more convolutional layers to account for the spatial aspect of the dataset then to use an LSTM layer to address the temporal aspect, but I have not yet worked out how this should be done using Keras/TF. Maybe there’s a better way to go about this using the fastai library?
I am a beginner with ML/DL, any ideas, even basics, are welcome. Thanks in advance for any suggestions, I appreciate your help.