Neural and spectral operator surrogates: unified construction and expression rate bounds
Approximation rates are analyzed for deep surrogates of maps between infinite-dimensional function spaces, arising, e.g., as data-to-solution maps of linear and nonlinear partial differential equations. Specifically, we study approximation rates for deep neural operator and generalized polynomial ch...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
15 July 2024
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| In: |
Advances in computational mathematics
Year: 2024, Volume: 50, Issue: 4, Pages: 1-43 |
| ISSN: | 1572-9044 |
| DOI: | 10.1007/s10444-024-10171-2 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s10444-024-10171-2
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| Author Notes: | Lukas Herrmann, Christoph Schwab, Jakob Zech |
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| 520 | |a Approximation rates are analyzed for deep surrogates of maps between infinite-dimensional function spaces, arising, e.g., as data-to-solution maps of linear and nonlinear partial differential equations. Specifically, we study approximation rates for deep neural operator and generalized polynomial chaos (gpc) Operator surrogates for nonlinear, holomorphic maps between infinite-dimensional, separable Hilbert spaces. Operator in- and outputs from function spaces are assumed to be parametrized by stable, affine representation systems. Admissible representation systems comprise orthonormal bases, Riesz bases, or suitable tight frames of the spaces under consideration. Algebraic expression rate bounds are established for both, deep neural and spectral operator surrogates acting in scales of separable Hilbert spaces containing domain and range of the map to be expressed, with finite Sobolev or Besov regularity. We illustrate the abstract concepts by expression rate bounds for the coefficient-to-solution map for a linear elliptic PDE on the torus. | ||
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| 650 | 4 | |a Operator learning | |
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