Learning to Integrate
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911005109387264 |
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| author | Ernst, Oliver G. Gottschalk, Hanno Kowalewitz, Toni Krüger, Patrick |
| author_facet | Ernst, Oliver G. Gottschalk, Hanno Kowalewitz, Toni Krüger, Patrick |
| contents | This work deals with uncertainty quantification for a generic input distribution to some resource-intensive simulation, e.g., requiring the solution of a partial differential equation. While efficient numerical methods exist to compute integrals for high-dimensional Gaussian and other separable distributions based on sparse grids (SG), input data arising in practice often does not fall into this class. We therefore employ transport maps to transform complex distributions to multivatiate standard normals. In generative learning, a number of neural network architectures have been introduced that accomplish this task approximately. Examples are affine coupling flows (ACF) and ordinary differential equation-based networks such as conditional flow matching (CFM). To compute the expectation of a quantity of interest, we numerically integrate the composition of the inverse of the learned transport map with the simulation code output. As this map is integrated over a multivariate Gaussian distribution, SG techniques can be applied. Viewing the images of the SG quadrature nodes as learned quadrature nodes for a given complex distribution motivates our title. We demonstrate our method for monomials of total degrees for which the unmapped SG rules are exact. We also apply our approach to the stationary diffusion equation with coefficients modeled by exponentiated Lévy random fields, using a Karhunen-Loève-like modal expansions with 9 and 25 modes. In a series of numerical experiments, we investigate errors due to learning accuracy, quadrature, statistical estimation, truncation of the modal series of the input random field, and training data size for three normalizing flows (ACF, conditional Flow Matching and Optimal transport Flow Matching) We discuss the mathematical assumptions on which our approach is based and demonstrate its shortcomings when these are violated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11801 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Integrate Ernst, Oliver G. Gottschalk, Hanno Kowalewitz, Toni Krüger, Patrick Numerical Analysis Probability 65C30 (Primary) 65D40, 68T07 (Secondary) This work deals with uncertainty quantification for a generic input distribution to some resource-intensive simulation, e.g., requiring the solution of a partial differential equation. While efficient numerical methods exist to compute integrals for high-dimensional Gaussian and other separable distributions based on sparse grids (SG), input data arising in practice often does not fall into this class. We therefore employ transport maps to transform complex distributions to multivatiate standard normals. In generative learning, a number of neural network architectures have been introduced that accomplish this task approximately. Examples are affine coupling flows (ACF) and ordinary differential equation-based networks such as conditional flow matching (CFM). To compute the expectation of a quantity of interest, we numerically integrate the composition of the inverse of the learned transport map with the simulation code output. As this map is integrated over a multivariate Gaussian distribution, SG techniques can be applied. Viewing the images of the SG quadrature nodes as learned quadrature nodes for a given complex distribution motivates our title. We demonstrate our method for monomials of total degrees for which the unmapped SG rules are exact. We also apply our approach to the stationary diffusion equation with coefficients modeled by exponentiated Lévy random fields, using a Karhunen-Loève-like modal expansions with 9 and 25 modes. In a series of numerical experiments, we investigate errors due to learning accuracy, quadrature, statistical estimation, truncation of the modal series of the input random field, and training data size for three normalizing flows (ACF, conditional Flow Matching and Optimal transport Flow Matching) We discuss the mathematical assumptions on which our approach is based and demonstrate its shortcomings when these are violated. |
| title | Learning to Integrate |
| topic | Numerical Analysis Probability 65C30 (Primary) 65D40, 68T07 (Secondary) |
| url | https://arxiv.org/abs/2506.11801 |