Spectral methods for nonlinear functionals and functional differential equations

We present a rigorous convergence analysis for cylindrical approximations of nonlinear functionals, functional derivatives, and functional differential equations (FDEs). The purpose of this analysis is twofold: First, we prove that continuous nonlinear functionals, functional derivatives, and FDEs c...

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Veröffentlicht in:Research in the mathematical sciences Jg. 8; H. 2
Hauptverfasser: Venturi, Daniele, Dektor, Alec
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Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.06.2021
Springer Nature B.V
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Abstract We present a rigorous convergence analysis for cylindrical approximations of nonlinear functionals, functional derivatives, and functional differential equations (FDEs). The purpose of this analysis is twofold: First, we prove that continuous nonlinear functionals, functional derivatives, and FDEs can be approximated uniformly on any compact subset of a real Banach space admitting a basis by high-dimensional multivariate functions and high-dimensional partial differential equations (PDEs), respectively. Second, we show that the convergence rate of such functional approximations can be exponential, depending on the regularity of the functional (in particular its Fréchet differentiability), and its domain. We also provide necessary and sufficient conditions for consistency, stability and convergence of cylindrical approximations to linear FDEs. These results open the possibility to utilize numerical techniques for high-dimensional systems such as deep neural networks and numerical tensor methods to approximate nonlinear functionals in terms of high-dimensional functions, and compute approximate solutions to FDEs by solving high-dimensional PDEs. Numerical examples are presented and discussed for prototype nonlinear functionals and for an initial value problem involving a linear FDE.
AbstractList We present a rigorous convergence analysis for cylindrical approximations of nonlinear functionals, functional derivatives, and functional differential equations (FDEs). The purpose of this analysis is twofold: First, we prove that continuous nonlinear functionals, functional derivatives, and FDEs can be approximated uniformly on any compact subset of a real Banach space admitting a basis by high-dimensional multivariate functions and high-dimensional partial differential equations (PDEs), respectively. Second, we show that the convergence rate of such functional approximations can be exponential, depending on the regularity of the functional (in particular its Fréchet differentiability), and its domain. We also provide necessary and sufficient conditions for consistency, stability and convergence of cylindrical approximations to linear FDEs. These results open the possibility to utilize numerical techniques for high-dimensional systems such as deep neural networks and numerical tensor methods to approximate nonlinear functionals in terms of high-dimensional functions, and compute approximate solutions to FDEs by solving high-dimensional PDEs. Numerical examples are presented and discussed for prototype nonlinear functionals and for an initial value problem involving a linear FDE.
ArticleNumber 27
Author Venturi, Daniele
Dektor, Alec
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  givenname: Alec
  surname: Dektor
  fullname: Dektor, Alec
  organization: Department of Applied Mathematics, University of California Santa Cruz
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crossref_primary_10_1007_s10915_021_01539_3
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Snippet We present a rigorous convergence analysis for cylindrical approximations of nonlinear functionals, functional derivatives, and functional differential...
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SubjectTerms Applications of Mathematics
Approximation
Artificial neural networks
Banach spaces
Boundary value problems
Computational Mathematics and Numerical Analysis
Continuity (mathematics)
Convergence
Derivatives
Functionals
Mathematics
Mathematics and Statistics
Numerical methods
Partial differential equations
Spectral methods
Tensors
Title Spectral methods for nonlinear functionals and functional differential equations
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