The Analytic Element Method (AEM) is an exotic groundwater modelling technique. Where numerical approaches require a grid to assemble a solution in small spatial increments, AEM does not rely on computing a grid of local solutions. Instead, this method constructs the full, global solution to the flow equations through super-position of analytic elements which project their influence over the entire flow domain.
The advantages of this are two-fold: First, this makes the method highly computationally efficient. Second, the method does not require enclosure through finite boundaries. Such boundaries are a computational necessity for numerical models, but rarely exist in reality. AEM allows us to induce regional flow without such rigid boundaries.
Both properties make AEM extremely well-suited for uncertainty estimation. To facilitate this better, a Python toolbox which implements a basic AEM routine exist and provides an in-built Markov Chain Monte Carlo (MCMC) algorithm. The hope of the developer Max Ramgraber is that this toolbox will allow hydrogeologists less familiar with Bayesian statistics (or other environmental scientists unfamiliar with hydrogeological modelling) to incorporate basic groundwater flow uncertainty estimates into their work. This toolbox has a corresponding publication.