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Neural and gpc operator surrogates: construction and expression rate bounds

by L. Herrmann and Ch. Schwab and J. Zech

(Report number 2022-27)

Abstract
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 gpc 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.

Keywords:

@Techreport{HSZ22_1015,
  author = {L. Herrmann and Ch. Schwab and J. Zech},
  title = {Neural and gpc operator surrogates: construction and expression rate bounds},
  institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich},
  number = {2022-27},
  address = {Switzerland},
  url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2022/2022-27.pdf },
  year = {2022}
}

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