any method that involves numerically solving discretized sparse linear operator equation systems, one would naturally not expect sensitivity backpropagation to be as readily applicable with these approaches as with Worldline Numerics. On the one hand, sensitivity backpropagating an iterative linear solver would require remembering the intermediate values from all iteration steps (otherwise thrown away), and also, the calculation may have happened to produce a solution before having explored some of the dependencies sufficiently well. While research has been done on backpropagating linear solvers, the incorporation of this strategy into Worldline Numerics undeniably is much easier to accomplish.

Adaptive Sampling {#subsec:adaptive}
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In a typical geometry, essentially the whole energy or force is contributed by few, comparably small regions. These are typically the regions where two objects come closely together.

While we still have to sample loops in such a way that we integrate over all of the relevant region of space, it is worthwhile to focus effort mainly on these highly contributing areas, as the absolute uncertainty of our Monte-Carlo estimation is much higher there. We achieve this in the following way: We first specify an absolute accuracy to which we want the density estimated to at every point. When 