I'm coding an application which is required to perform an n-body simulation between a few hundred particles which are constantly in motion. The application has real time requirements and thus the algorithm performing the simulation needs to be fast.
I've done a fair amount of research on the matter and have come to the conclusion that the Barnes Hut algorithm would be most suitable for my needs, it seems very efficient for large particle sets.
http://arborjs.org/docs/barnes-hut gave a very clear explanation on how the algorithm worked, but as the title implies, I'd like to know whether the tree needs to be recreated for each iteration, considering that the particles used in the simulation are always dynamically in motion. And if the tree does need to be recreated, how does one do it in the most efficient (in terms of processing power and memory) way.
Usually with motion based indexes there is no "update" for the index after movement has occurred and you must rebuild the entire index.
The Barnes Hut Tree is the same and will have to be rebuilt. Here is an example I found online with a code outline of the process.
This is one of the reason so much effort has gone into build optimizations for things like the KD-Tree, and I'm sure the Barnes Hut has the same. Also, I'm sure there is research on dynamically updating, but most of the time these implementations are much harder than a simply rebuilding.