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Generalized FNO approximation on compact sets
Statement
Assume Countable Choice. Let for a nonempty bounded smooth domain , , and let be positive finite channel dimensions. If is continuous, then for every compact set and every , there exists a generalized Fourier neural operator using a fixed smooth globally Lipschitz nonpolynomial activation and the boundary charts of the statement such that
Statement references
Facts & Assumptions
Given: , , , compact , and as in the statement.
There are bounded chart maps and in the reverse direction, obtained by applying the scalar chart maps componentwise, with . (Boundary chart lifting and compact-uniform Fourier projections)
On a torus, a Fourier neural operator with the specified activation uniformly approximates every continuous map on every compact input set in the norm. (Fourier neural operators approximate continuous maps uniformly on compact torus sets)
Zero extension from to is isometric because ; the product extension constant in the last coordinates is also isometric under normalized Haar measure.
The generalized-FNO architecture contains the zero operator by setting its finite-layer coefficients to zero. (Generalized Fourier neural operator on a compact manifold)
Countable Choice is assumed at the finite boundary-chart partition interface. (The Axiom of Countable Choice ())
Proof
Given: Countable Choice and the data in the statement.
If , choose the zero generalized FNO from [F4]; the uniform error condition is vacuous. Otherwise let and let be zero extension. Let send to the function constant in the last coordinates. The chart-pair components identify with . Define on this direct-sum space. This is continuous by [F1] and [F3], and is compact because is continuous and is compact.
Apply [F2] to the continuous map on compact with tolerance . There is a torus FNO such that .
Set with unit mass, so the following integral is the identity when . For any finite channel dimension , define by , componentwise. Cauchy–Schwarz on the unit-measure torus and Fubini give . Let be restriction from to , also of norm at most one. Since , , and , the generalized FNO satisfies for every . Write the torus FNO as , where ends before its final constant affine channel map and has output channels. Its initial affine channel lift is composed into the first affine part of after . Normalized averaging commutes with the final map, , and restriction commutes with the same pointwise map. Hence . This has the generalized architecture: identity pointwise lift, chart lift , torus layers , the constant-kernel dimension reduction , restriction , and final pointwise affine map .
Depends on
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- Generalized Fourier neural operator on a compact manifold
- Boundary chart lifting and compact-uniform Fourier projections
- Finite-dimensional universal approximation by smooth nonpolynomial activations
- Fourier neural operators approximate continuous maps uniformly on compact torus sets
Used by
Dependency tree · two levels
16 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Maarten V. de Hoop, Nikola B. Kovachki, Matti Lassas and Nicholas H. Nelsen, Extension and neural operator approximation of the electrical impedance tomography inverse map (standard reference, not scraped)
- Nikola Kovachki et al., On Universal Approximation and Error Bounds for Fourier Neural Operators (standard reference, not scraped)