Just a quick speculation.
If you use AI.Thesaurus thinking . Then gravity is close to minimization of distances. So at each iteration you got some force to physically bringing objects closer to each other.
Bringing things closer in machine learning terms means getting a lower loss. So to solve this simple equation I assume a modelF() for F and different modelG() for G. Then since a machine learning model can assume any function their complex behaviour could solve this equation at any distance r.
Basically you got F.mean_squared_error(modelF()*r**2, modelG()*m1*m2). That is G ?is some dynamically update possible constant.
So the idea is that both modelF and modelG will start to work together to solve the equation or loss function.