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In this monograph we develop magnetic pseudodifferential theory for operator-valued and equivariant operator-valued functions and distributions from first principles. These have found plentiful applications in mathematical physics,…

Mathematical Physics · Physics 2022-10-13 Giuseppe De Nittis , Max Lein , Marcello Seri

The aim of this paper is to show that various known characterizations of traces on classical pseudodifferentials operators (PsiDOs) can actually be obtained by very elementary considerations on PsiDOs, using only basic properties of these…

Analysis of PDEs · Mathematics 2010-09-17 Raphael Ponge

These lecture notes are intended as an introduction to the theory of rational Dunkl operators and the associated special functions, with an emphasis on positivity and asymptotics. We start with an outline of the general concepts: Dunkl…

Classical Analysis and ODEs · Mathematics 2007-05-23 Margit Rösler

The purpose of this paper is to obtain microlocal analogues of results by L. H \"ormander about inclusion relations between the ranges of first order differential operators with coefficients in $C^\infty$ which fail to be locally solvable.…

Analysis of PDEs · Mathematics 2015-02-13 Jens Wittsten

Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based…

Machine Learning · Computer Science 2024-03-18 Ashutosh Singh , Ricardo Augusto Borsoi , Deniz Erdogmus , Tales Imbiriba

In this chapter we provide an introduction to fractional dissipative partial differential equations (PDEs) with a focus on trying to understand their dynamics. The class of PDEs we focus on are reaction-diffusion equations but we also…

Neural Operators (NOs) provide a powerful framework for computations involving physical laws that can be modelled by (integro-) partial differential equations (PDEs), directly learning maps between infinite-dimensional function spaces that…

Machine Learning · Computer Science 2025-09-18 Gianluca Fabiani , Hannes Vandecasteele , Somdatta Goswami , Constantinos Siettos , Ioannis G. Kevrekidis

This book is intended as a self-contained introduction to selected topics in the fractional world, focusing particularly on aspects that arise in the study of equations driven by the fractional Laplacian. The scope of this work is not…

Analysis of PDEs · Mathematics 2025-01-15 Nicola Abatangelo , Serena Dipierro , Enrico Valdinoci

The solution of pseudo initial value differential equations, either ordinary or partial (including those of fractional nature), requires the development of adequate analytical methods, complementing those well established in the ordinary…

Mathematical Physics · Physics 2019-02-05 Nicolas Behr , Giuseppe Dattoli , Ambra Lattanzi

These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics.…

Machine Learning · Computer Science 2023-01-04 Deep Ray , Orazio Pinti , Assad A. Oberai

Neural operators (NOs) provide a new paradigm for efficiently solving partial differential equations (PDEs), but their training depends on costly high-fidelity data from numerical solvers, limiting applications in complex systems. We…

Computational Physics · Physics 2026-05-18 Wen You , Shaoqian Zhou , Xuhui Meng

This paper is the first part of a two-paper series whose aim is to give a thorough account on Connes' pseudodifferential calculus on noncommutative tori. This pseudodifferential calculus has been used in numerous recent papers, but a…

Operator Algebras · Mathematics 2019-04-09 Hyunsu Ha , Gihyun Lee , Raphael Ponge

Pseudo-differential operator equations with parameter are studied. Uniform separability properties and resolvent estimates are obtained in terms of fractional derivatives. Moreover, maximal regularity properties of the pseudo-differential…

Analysis of PDEs · Mathematics 2017-06-06 Veli Shakhmurov

This paper proposes a novel deep learning approach for learning operators in semigroup, with applications to modeling unknown autonomous dynamical systems using time series data collected at varied time lags. It is a sequel to the previous…

Machine Learning · Computer Science 2023-09-13 Junfeng Chen , Kailiang Wu

These lecture notes provide an introduction to the theory and application of symmetry methods for ordinary differential equations, building on minimal prerequisites. Their primary purpose is to enable a quick and self-contained approach for…

Classical Analysis and ODEs · Mathematics 2023-04-03 Sebastian Walcher

This thesis details a project to define a fully compositional theory of synchronous sequential circuits built from primitive components, motivated by applying techniques successfully used in programming languages to hardware. The first part…

Logic in Computer Science · Computer Science 2025-02-13 George Kaye

The paper focuses on various properties and applications of the homotopy operator, which occurs in the Poincar\'{e} lemma. In the first part, an abstract operator calculus is constructed, where the exterior derivative is an abstract…

Differential Geometry · Mathematics 2020-07-14 Radosław Antoni Kycia

Lecture note topics: 1. Some tools from real and complex analysis, 2. Hilbert spaces, 3. Banach spaces, 4. Compact operators and their spectra, 5. Intermezzo: reproducing kernel Hilbert spaces, 6. Banach algebras ,7. Spectral theory of…

Functional Analysis · Mathematics 2025-05-21 Nicola Arcozzi

In this work we produce microlocal normal forms for pseudodifferential operators which have a Lagrangian submanifold of radial points. This answers natural questions about such operators and their associated classical dynamics. In a sequel,…

Analysis of PDEs · Mathematics 2012-10-05 Nick Haber

The purpose of this note is to review some recent results concerning the pseudospectra and the eigenvalues asymptotics of non-selfadjoint semiclassical pseudo-differential operators subject to small random perturbations.

Spectral Theory · Mathematics 2024-10-08 Martin Vogel