What does forward modeling mean?
In my research on exoplanets, I have heard many people talk "forward modeling of exoplanet atmospheres". I don't know what the "forward" means in "forward modeling" and how it compares with "reverse modeling", if that's even a thing.
What is forward modeling, and why is it so special that it needs to be distinguished from just plain ol' regular modeling?
There are different ways to model something. From what you're asking, there are two main types of modeling: forward modeling and inverse modeling.
In this type of modeling, you have a specific model that defines the "current" state of your system. In the case of exoplanet atmospheres, it'd likely be something that defines the molecular content, ionization level, density, etc. of your exoplanet atmosphere. Then, you use the known physics/math of your system to decide how it will behave. In this setup, what you've created is a system for predicting system states from a predetermined physics model.
Such an example would be someone creating their own atmosphere of an exoplanet in a model and then saying, okay what happens when I shine light through this atmosphere. What observations might I record?
In some sense this is the opposite of forward modeling, albeit it doesn't really mean you're running a model to see into the past. Instead, what happens with this setup is you know a particular state or result, and you want to construct a model of your system which can produce said state. Essentially, you want your model to arrive at a certain state when it is done calculating. If it does, you have a reasonable confidence that your model was some indication of what your system is actually like.
In this situation, you'd measure components of the atmosphere, e.g. the radius of the planet as a function of wavelength, and then create a model of the atmosphere which can hopefully reproduce your observations. If you can, then the hope is that the model accurately represents what your system is.
It seems to me that one could be producing the same models in both the forward and inverse modeling case, just in the forward modeling case you're trying to predict what you might see (simulated data) and the inverse case you're trying to understand what you do see (real data). Is this the case? And if so, why is the distinction between forward and inverse modeling important and/or useful?
@Joshua Yes, you're right that the same model could be used in both cases. The distinction comes in what you're trying to achieve and what data you have to work with. Take the example of modeling the planetary radius vs wavelength. In the forward case, you would create a model and say what observations would I expect to make in real life, from this model (i.e., you *don't* work with observations). In the inverse case, you already have measurements of planet radius vs wavelength and you'd create a model to reproduce those measurements and then say your model accurately modeled the system.