Machine Learning Discovery of Optimal Quadrature Rules for Isogeometric Analysis
This work addresses computational efficiency in isogeometric analysis for engineering simulations, offering significant cost reductions, though it is incremental as it builds on existing quadrature methods with a novel optimization approach.
The paper tackles the problem of finding optimal quadrature rules for isogeometric analysis by posing it as an optimization problem solved with a machine learning strategy based on gradient-descent and dynamic programming, achieving savings of up to 44% in 1D, 68% in 2D, and 82% in 3D compared to traditional Gaussian integration.
We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially consider 1D spline spaces of arbitrary degree spanned over uniform and non-uniform knot sequences, and then the generated optimal rules are used for integration over higher-dimensional spaces using tensor product sense. The quadrature rule search is posed as an optimization problem and solved by a machine learning strategy based on gradient-descent. However, since the optimization space is highly non-convex, the success of the search strongly depends on the number of quadrature points and the parameter initialization. Thus, we use a dynamic programming strategy that initializes the parameters from the optimal solution over the spline space with a lower number of knots. With this method, we found optimal quadrature rules for spline spaces when using IGA discretizations with up to 50 uniform elements and polynomial degrees up to 8, showing the generality of the approach in this scenario. For non-uniform partitions, the method also finds an optimal rule in a reasonable number of test cases. We also assess the generated optimal rules in two practical case studies, namely, the eigenvalue problem of the Laplace operator and the eigenfrequency analysis of freeform curved beams, where the latter problem shows the applicability of the method to curved geometries. In particular, the proposed method results in savings with respect to traditional Gaussian integration of up to 44% in 1D, 68% in 2D, and 82% in 3D spaces.