NeuralPDR should be able to be trained for two default cases, full time-series predictions and predicting a single time step as chosen by the user. Predicting a single time step can either be done by using a dedicated NN (the time of the prediction is fixed) or by using the NeuralODE architecture (the time of the prediction is variable).
- Note that time and visual extinction $A_v$ (depth) are used interchangeable a lot.
Full timeseries
It has a series of Temperatures, densities, Radiation with initial conditions of the chemisty ($x_i$). And wanting a prediction of the chemistry for some time t=10 Million years and points in between. This is now the default use case in NeuralPDR.
- Useful for postprocessing simulations and fundamental research
Single time step
Want the chemistry at a time / point in space, typically without any changes to temperature, density and radiation etc.
- Useful for observational fitting. Assume steady state chemistry (or at least fixed)
NeuralPDR should be able to be trained for two default cases, full time-series predictions and predicting a single time step as chosen by the user. Predicting a single time step can either be done by using a dedicated NN (the time of the prediction is fixed) or by using the NeuralODE architecture (the time of the prediction is variable).
Full timeseries
It has a series of Temperatures, densities, Radiation with initial conditions of the chemisty ($x_i$ ). And wanting a prediction of the chemistry for some time t=10 Million years and points in between. This is now the default use case in NeuralPDR.
Single time step
Want the chemistry at a time / point in space, typically without any changes to temperature, density and radiation etc.