Neural network-enhanced $hr$-adaptive finite element algorithm for parabolic equations

In this paper, we propose a novel $hr$-adaptive finite element method, enhanced by neural networks, for parabolic equations.

The main challenge of the conventional $h$-adaptive finite element method is interpolating the finite element solution from the previous step in the updated mesh.

The interpolation dependent on the new mesh must be recomputed at each adaptive iteration, resulting in high computational costs.

The new approach addresses this challenge by introducing a neural network to construct a mesh-free surrogate of the previous step finite element solution.

Since the neural network is mesh-free, it only requires training once per time step, with its parameters initialized using the minimizer of the previous time step.

This approach effectively overcomes the interpolation challenges associated with non-nested meshes in computation, making node insertion and movement more convenient and efficient.

The new algorithm also emphasizes SIZE and GENERATE, allowing each refinement to roughly double the number of mesh nodes of the previous iteration and then redistribute them to form a new mesh that effectively captures the singularities.

It significantly reduces the time required for repeated refinement of the conventional methods and achieves the desired accuracy in no more than seven space-adaptive iterations per time step.

Numerical experiments confirm the efficiency of the proposed algorithm in capturing dynamic changes of singularities.

The code is made publicly available on GitHub.