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We illustrate the composition properties for an extended family of SG Fourier integral operators. We prove continuity results for operators in this class with respect to $L^2$ and weighted modulation spaces, and discuss continuity on…

Functional Analysis · Mathematics 2020-03-03 S. Coriasco , K. Johansson , J. Toft

A plentitude of applications in scientific computing requires the approximation of mappings between Banach spaces. Recently introduced Fourier Neural Operator (FNO) and Deep Operator Network (DeepONet) can provide this functionality. For…

Numerical Analysis · Mathematics 2024-04-02 V. Fanaskov , I. Oseledets

Non-self-adjoint second-order ordinary differential operators on a finite interval with complex weights are studied. Properties of spectral characteristics are established and the inverse problem of recovering operators from their spectral…

Spectral Theory · Mathematics 2024-02-09 V. A. Yurko

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…

Numerical Analysis · Mathematics 2024-02-09 Lukas Herrmann , Christoph Schwab , Jakob Zech

We introduce a comprehensive framework for subordinacy theory applicable to long-range operators on $\ell^2(\mathbb Z)$, bridging dynamical systems and spectral analysis. For finite-range operators, we establish a correspondence between the…

Dynamical Systems · Mathematics 2025-07-01 Zhenfu Wang , Disheng Xu , Qi Zhou

In this paper, we study the consequences of the fundamental theorem of calculus from an algebraic point of view. For functions with singularities, this leads to a generalized notion of evaluation. We investigate properties of such…

Rings and Algebras · Mathematics 2025-01-20 Clemens G. Raab , Georg Regensburger

Operator learning offers a powerful paradigm for solving parametric partial differential equations (PDEs), but scaling probabilistic neural operators such as the recently proposed Gaussian Processes Operators (GPOs) to high-dimensional,…

Machine Learning · Statistics 2025-06-23 Sawan Kumar , Tapas Tripura , Rajdip Nayek , Souvik Chakraborty

We present a model for spectral theory of families of selfadjoint operators, and their corresponding unitary one-parameter groups (acting in Hilbert space.) The models allow for a scale of complexity, indexed by the natural numbers…

Spectral Theory · Mathematics 2012-02-21 Palle Jorgensen , Steen Pedersen , Feng Tian

The classical spectral theorem completely describes self-adjoint operators on finite dimensional inner product vector spaces as linear combinations of orthogonal projections onto pairwise orthogonal subspaces. We prove a similar theorem for…

Rings and Algebras · Mathematics 2017-10-03 Camilo Sanabria Malagón

In this paper, we present a complete spectral research of generalized Ces\`aro operators on Sobolev-Lebesgue sequence spaces. The main idea is to subordinate such operators to suitable $C_0$-semigroups on these sequence spaces. We introduce…

Functional Analysis · Mathematics 2017-04-25 Luciano Abadia , Pedro J. Miana

Indicial operators are model operators associated to an elliptic differential operator near a corner singularity on a stratified manifold. These model operators are defined on generalized tangent cone configurations and exhibit a natural…

Analysis of PDEs · Mathematics 2021-07-06 Thomas Krainer

In the paper the general case of a normal discrete Hausdorff operators in $L^2(\mathbb{R}^d)$ is considered. The main result states that under some natural arithmetic condition the spectrum of such an operator is rotationally invariant.…

Functional Analysis · Mathematics 2023-07-12 A. R. Mirotin

Designing universal artificial intelligence (AI) solver for partial differential equations (PDEs) is an open-ended problem and a significant challenge in science and engineering. Currently, data-driven solvers have achieved great success,…

Machine Learning · Computer Science 2025-02-24 Qinglong Ma , Peizhi Zhao , Sen Wang , Tao Song

Schr\"odinger operators with periodic (possibly complex-valued) potentials and discrete periodic operators (possibly with complex-valued entries) are considered, and in both cases the computational spectral problem is investigated: namely,…

Spectral Theory · Mathematics 2021-04-21 Jonathan Ben-Artzi , Marco Marletta , Frank Rösler

We introduce a notion of an algebra of generalized pseudo-differential operators and prove that a spectral triple is regular if and only if it admits an algebra of generalized pseudo-differential operators. We also provide a self-contained…

Operator Algebras · Mathematics 2011-11-11 Otgonbayar Uuye

It was recently established that the formalism of the generalized transfer operator (GTO) of dynamical systems (DS) theory, applied to stochastic differential equations (SDEs) of arbitrary form, belongs to the family of cohomological…

Mathematical Physics · Physics 2025-12-29 Igor V. Ovchinnikov

We analyze the spectrum of the operator $\Delta^{-1} [\nabla \cdot (K\nabla u)]$, where $\Delta$ denotes the Laplacian and $K=K(x,y)$ is a symmetric tensor. Our main result shows that this spectrum can be derived from the spectral…

Analysis of PDEs · Mathematics 2020-02-04 Tomáš Gergelits , Bjørn Fredrik Nielsen , Zdeněk Strakoš

This paper proves a genericity conjecture by Goldstein, Schlag, and Voda[Invent. Math.\textbf{217}(2019)] for multi-frequency quasiperiodic Schr\"{o}dinger operators. Specifically, we show that for almost all coefficients of real…

Spectral Theory · Mathematics 2026-05-07 Daxiong Piao

The structure of the spectrum of random operators is studied. It is shown that if the density of states measure of some subsets of the spectrum is zero, then these subsets are empty. In particular follows that absolute continuity of the IDS…

Spectral Theory · Mathematics 2015-06-15 Rafael del Rio

Deep learning methods have access to be employed for solving physical systems governed by parametric partial differential equations (PDEs) due to massive scientific data. It has been refined to operator learning that focuses on learning…

Machine Learning · Computer Science 2024-03-06 Chu Wang , Jinhong Wu , Yanzhi Wang , Zhijian Zha , Qi Zhou
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