How to model distance and interpret predictors I have a set of 2 - 10 explanatory variables which I'd like to use to predict the response variable, distance. The explanatory variables describe the flight of a projectile (velocity, spin, angle) and the conditions of the air it is flying through (temperature, altitude, etc.). I'd like to end up with an interpretable model that makes the best predictions possible, though I know these don't always go hand-in-hand. For example, I want to know much an increase/decrease in sidespin affects distance.
Now, this almost sounds like an ideal problem for linear regression, but the issue is the relationship between distance and angle, for example, is not linear, as if the ball is launched at too high of an angle (say 90 degrees straight up), obviously the distance will be small. In fact, I've tried a linear model already in which I square the angle term (and include some interaction terms), but the predictions are worse than I would like. Furthermore, since linear regression outputs a "line" in multi-dimensional space, it makes some negative predictions, and taking log() still doesn't help the predictions all that much.
Now, I talked to some people and we thought a neural network might be a good choice, since neural nets are very good approximators of functions, and I believe there is some underlying non-linear function at play here. Some of the neural nets I've tried so far give much better predictions than the linear model, however it obviously isn't easy to interpret the main effects.
Does anyone have any thoughts as for the overall approach here? Could it make sense to use a linear model to interpret the influence of the main effects on distance while then using a neural net to make predictions?

If you can, you would benefit from defining your models based on physical principles, to properly capture the nonlinear relationships you are interested in.
If that is not possible for all predictors, a simple alternative would be to use generalized-additive-models (GAMs), which are linear models that allow you to model responses as smooth, nonlinear functions of predictors. So distance can be made a smooth function of angle (and any other predictors), without specifying the functional form. Other predictors can be constrained to follow linear relationships, as in a linear regression.
You can also use neural networks or other machine learning methods, and then generate partial dependence plots to visualise the nonlinear response to specific predictors. This is a key trick in what is called 'interpretable machine learning', for which I recommend this free online book by Christoph Molnar.
