Add technical implementation in jax documentation

VERSION

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-0.0.12
+0.0.13

docs/how_it_works.md

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 # Technical implementation in `jax`
+This package uses `jax` as its backend for generating synthetic curves.
+In particular, `jax` is used for:
+
+- Solving non-linear optimization problems.
+- Automatic differentiation for calculating partial derivates of `f`.
+- Just-in-time (JIT)-compilation with XLA for performance optimization.
+
+For more detailed information regarding the XLA-compilation, please see the 
+[offical `jax` documenation](https://docs.jax.dev/en/latest/index.html)
+or [XLA-documentation](https://openxla.org/xla/tf2xla?hl=en).
+
+These three points ensure an efficient generation of curves, while being
+able to control the latent information used and the behaviour of drifts applied
+on the curves.
+
+The method used to solve optimization problems is the 
+[LBFGS](https://en.wikipedia.org/wiki/Limited-memory_BFGS) algorithm, which is supported by `jax`.
+The corresponding functions in order to compute the error term, running a iteration of the
+optimization solving problem, and computing the gradients is all done in functions which are 
+compiled just-in-time and can be run on a GPU.
+The procedure can be described as follows:
+
+- Choose a function $f(w(t), x)$, which describes the shape of the curves to generate with 
+inital internal parameters $w_0(t)$.
+- Provide problem constraints encoded in
+[`driftbench.data_generation.latent_information.LatentInformation`][driftbench.data_generation.latent_information.LatentInformation]
+objects.
+- Compute the $i$ partial derivates of $f(w(t), x)$ with respect to $x$.
+- For each latent information object, compute $w(t)$ according to the LBFGS-algorithm.
+- Return all computed solutions $w(t)$ for each curve.
+