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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.20956 |
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| _version_ | 1866909975324917760 |
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| author | Yang, Xianjin Darcy, Matthieu Hudes, Matthew Alexander, Francis J. Eyink, Gregory Owhadi, Houman |
| author_facet | Yang, Xianjin Darcy, Matthieu Hudes, Matthew Alexander, Francis J. Eyink, Gregory Owhadi, Houman |
| contents | We present an operator learning framework for solving non-perturbative functional renormalization group equations, which are integro-differential equations defined on functionals. Our proposed approach uses Gaussian process operator learning to construct a flexible functional representation formulated directly on function space, making it independent of a particular equation or discretization. Our method is flexible, and can apply to a broad range of functional differential equations while still allowing for the incorporation of physical priors in either the prior mean or the kernel design. We demonstrate the performance of our method on several relevant equations, such as the Wetterich and Wilson--Polchinski equations, showing that it achieves equal or better performance than existing approximations such as the local-potential approximation, while being significantly more flexible. In particular, our method can handle non-constant fields, making it promising for the study of more complex field configurations, such as instantons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20956 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solving Functional PDEs with Gaussian Processes and Applications to Functional Renormalization Group Equations Yang, Xianjin Darcy, Matthieu Hudes, Matthew Alexander, Francis J. Eyink, Gregory Owhadi, Houman Machine Learning We present an operator learning framework for solving non-perturbative functional renormalization group equations, which are integro-differential equations defined on functionals. Our proposed approach uses Gaussian process operator learning to construct a flexible functional representation formulated directly on function space, making it independent of a particular equation or discretization. Our method is flexible, and can apply to a broad range of functional differential equations while still allowing for the incorporation of physical priors in either the prior mean or the kernel design. We demonstrate the performance of our method on several relevant equations, such as the Wetterich and Wilson--Polchinski equations, showing that it achieves equal or better performance than existing approximations such as the local-potential approximation, while being significantly more flexible. In particular, our method can handle non-constant fields, making it promising for the study of more complex field configurations, such as instantons. |
| title | Solving Functional PDEs with Gaussian Processes and Applications to Functional Renormalization Group Equations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.20956 |