Learning rules for finite element mesh design

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Introduction to finite element methods

Finite element methods are used extensively by engineers and other modellers to analyse stresses in physical structures. These structures are represented quantitatively as finite collections of elements whose deformations can then be computed using linear algebraic equations.

In order to design a numerical model of a physical structure, the modeller must decide the appropriate resolution for modelling each component part, a task requiring considerable expertise. Too fine a mesh will cause unnecessary computational overheads when running the model, whereas too coarse a mesh will produce intolerable approximation errors.

We have used ILP to induce, from examples provided by expert modellers, rules for choosing appropriate resolution values. One advantage of ILP is that the examples and rules are expressed in predicate logic, so predicates can be used to describe geometric relations between different elements. Without such expressiveness, it would be impossible to adequately describe the structure being modelled.

Learning rules for the number of elements

The resolution of a FE mesh is determined by the number of elements on each of its edges. The problem of learning rules for determining the resolution of a FE mesh is therefore, to learn rules that determine the number of elements on an edge.

The data here is from experiments conducted with Golem as reported in [Dolsak B. and Muggleton S. (1992)].

The task is to learn rules for the number of elements using the following information:

  • The training examples have the form mesh(Edge,Number_of_elements), where Edge is an edge label (unique for each edge) and Number_of_elements is the number of elements on the edge denoted by label Edge. The number of elements on an edge varies from 1 to 17.
  • The background knowledge describes some of the factors that influence the resolution of a FE mesh, such as the type of edges, boundary conditions and loadings, as well as the shape of the structure (relations of neighborhood etc.).
  • According to its importance and geometric shape, an edge can belong to one of the following types: important_long, important, important_short, not_important, circuit, half_circuit, quarter_circuit, short_for_hole, long_for_hole, circuit_hole, half_circuit_hole and quarter_circuit_hole.
  • With respect to the boundary conditions an edge can be free, one_side_fixed, two_side_fixed or fixed.
  • According to the loadings an edge is not_loaded, one_side_loaded, two_side_loaded or continuously_loaded.
  • Background knowledge about the shape of a structure includes the symmetric relations neighbour/2 and opposite/2, as well as the relation equal/2.

The Golem dataset

The data concerns five structures labelled ``a'' -- ``e''. The data files are as used in the original Golem experiments, and are downloadable as one compressed TAR file. Within this file, background knowledge files have a ``.b'' suffix, positive example files have a ``.f'' suffix, and negative example files have a ``.n'' suffix.


Dolsak B. and Muggleton S. (1992).
The application of Inductive Logic Programming to finite element mesh design.
In S. Muggleton editor, Inductive Logic Programming, Academic Press, London.

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