Accelerating goal-oriented error estimation using neural networks
This repo emulates the error estimation step for anisotropic goal-oriented mesh adaptation using artificial neural networks.
This is research of the Applied Modelling and Computation Group (AMCG) at Imperial College London.
Currently, the various hyperparameters that define the network are chosen manually. It would be great to apply a package that would auto-tune them.
Currently, the turbine model solves the steady shallow water equations using a full LU decomposition at each nonlinear iteration. This is fine at low resolution, but gets expensive because the LU factorisation is O(n^3). It would be good if we could devise an alternative iterative solver strategy that is more scalable.
At the minute, we only support fully connected feed-forward networks with a single hidden layer. It would be good to build on this.
See the TODO in this commit.
At the minute, the code is heavily geared towards steady-state turbine examples. It should be made more general, to facilitate its application to other setups.
See the TODO in this commit.
At the minute, we only support enrichment by global h-refinement. It would be good to consider other approaches.
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