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# Possible extensions
#### Anisotropic triangulation would improve the algorithm.
The current implementation of choosing the candidate point inside a simplex (triangle for 2D) with the highest loss, for the `LearnerND`, works by either picking a point (1) in the center of the simplex or (2) by picking a point on the longest edge of the simplex.
The current implementation of choosing the candidate point inside a simplex (triangle in 2D) with the highest loss, for the `LearnerND`, works by either picking a point (1) in the center of the simplex or (2) by picking a point on the longest edge of the simplex.
The choice depends on the shape of the simplex, where the algorithm tries to create regular simplices.
Alternatively, a good strategy is choosing points somewhere on the edge of a triangle such that the simplex aligns with the gradient of the function; creating an anisotropic triangulation[@dyn1990data].
This is a similar approach to the anisotropic meshing techniques mentioned in the literature review.
We have started to implement this feature in Adaptive, however, there are still some unsolved problems.
We have started to implement this feature in Adaptive, however, there some unsolved problems remain.
#### Learning stochastic functions is a promising direction.
Stochastic functions frequently appear in numerical sciences.
Currently, Adaptive has a `AverageLearner` that samples a stochastic function with no degrees of freedom until a certain standard error of the mean is reached.
This is advantageous because no predetermined number of samples has to be set before starting the simulation.
Extending this learner to be able to deal with more dimensions would be a useful addition.
There is an effort to implement an `AverageLearner1D` and `AverageLearner2D`, however, it requires more work to make it reliable.
#### Experimental control needs to deal with noise, hysteresis, and the cost for changing parameters.
