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1. Local operator algebras of charged states in gauge theory and gravity

   https://doi.org/10.1007/JHEP03(2025)175  

   Grassi, P A; Porrati, M (March 2025, Journal of High Energy Physics)

   Multiple (Ed.)

   A<sc>bstract</sc> Powerful techniques have been developed in quantum field theory that employ algebras of local operators, yet local operators cannot create physical charged states in gauge theory or physical nonzero-energy states in perturbative quantum gravity. A common method to obtain physical operators out of local ones is to dress the latter using appropriate Wilson lines. This procedure destroys locality, it must be done case by case for each charged operator in the algebra, and it rapidly becomes cumbersome, particularly in perturbative quantum gravity. In this paper we present an alternative approach to the definition of physical charged operators: we define an automorphism that maps an algebra of local charged operators into a (non-local) algebra of physical charged operators. The automorphism is described by a formally unitary intertwiner mapping the exact BRS operator associated to the gauge symmetry into its quadratic part. The existence of an automorphism between local operators and the physical ones, describing charged states, allows to retain many of the results derived in local operator algebras and extend them to the physical-but-nonlocal algebra of charged operators as we discuss in some simple applications of our construction. We also discuss a formal construction of physical states and possible obstructions to it. 

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2. M$$M$$‐Ideals in real operator algebras

   https://doi.org/10.1002/mana.202400227  

   Blecher, David_P; Neal, Matthew; Peralta, Antonio_M; Su, Shanshan (March 2025, Mathematische Nachrichten)

   Abstract In a recent paper, we showed that a subspace of a real ‐triple is an ‐summand if and only if it is a ‐closed triple ideal. As a consequence, ‐ideals of real ‐triples, including real ‐algebras, real ‐algebras and real TROs, correspond to norm‐closed triple ideals. In this paper, we extend this result by identifying the ‐ideals in (possibly non‐self‐adjoint) real operator algebras and Jordan operator algebras. The argument for this is necessarily different. We also give simple characterizations of one‐sided ‐ideals in real operator algebras, and give some applications to that theory. 

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3. Radial Basis Approximation of Tensor Fields on Manifolds: From Operator Estimation to Manifold Learning

   Harlim, John; Jiang, Shixiao W; Peoples, John W (November 2023, Journal of machine learning research)

   Mahoney, Michael (Ed.)

   In this paper, we study the Radial Basis Function (RBF) approximation to differential operators on smooth tensor fields defined on closed Riemannian submanifolds of Euclidean space, identified by randomly sampled point cloud data. The formulation in this paper leverages a fundamental fact that the covariant derivative on a submanifold is the projection of the directional derivative in the ambient Euclidean space onto the tangent space of the submanifold. To differentiate a test function (or vector field) on the submanifold with respect to the Euclidean metric, the RBF interpolation is applied to extend the function (or vector field) in the ambient Euclidean space. When the manifolds are unknown, we develop an improved second-order local SVD technique for estimating local tangent spaces on the manifold. When the classical pointwise non-symmetric RBF formulation is used to solve Laplacian eigenvalue problems, we found that while accurate estimation of the leading spectra can be obtained with large enough data, such an approximation often produces irrelevant complex-valued spectra (or pollution) as the true spectra are real-valued and positive. To avoid such an issue, we introduce a symmetric RBF discrete approximation of the Laplacians induced by a weak formulation on appropriate Hilbert spaces. Unlike the non-symmetric approximation, this formulation guarantees non-negative real-valued spectra and the orthogonality of the eigenvectors. Theoretically, we establish the convergence of the eigenpairs of both the Laplace-Beltrami operator and Bochner Laplacian for the symmetric formulation in the limit of large data with convergence rates. Numerically, we provide supporting examples for approximations of the Laplace-Beltrami operator and various vector Laplacians, including the Bochner, Hodge, and Lichnerowicz Laplacians. 

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4. NON-LINEAR OPERATOR APPROXIMATIONS FOR INITIAL VALUE PROBLEMS

   Gupta, G.; Xiao, X.; Balan, R.; Bogdan, P. (January 2022, International Conference of Learning Representations (ICLR))

   Time-evolution of partial differential equations is fundamental for modeling several complex dynamical processes and events forecasting, but the operators associated with such problems are non-linear. We propose a Pad´e approximation based exponential neural operator scheme for efficiently learning the map between a given initial condition and the activities at a later time. The multiwavelets bases are used for space discretization. By explicitly embedding the exponential operators in the model, we reduce the training parameters and make it more data-efficient which is essential in dealing with scarce and noisy real-world datasets. The Pad´e exponential operator uses a recurrent structure with shared parameters to model the non-linearity compared to recent neural operators that rely on using multiple linear operator layers in succession. We show theoretically that the gradients associated with the recurrent Pad´e network are bounded across the recurrent horizon. We perform experiments on non-linear systems such as Korteweg-de Vries (KdV) and Kuramoto–Sivashinsky (KS) equations to show that the proposed approach achieves the best performance and at the same time is data-efficient. We also show that urgent real-world problems like epidemic forecasting (for example, COVID- 19) can be formulated as a 2D time-varying operator problem. The proposed Pad´e exponential operators yield better prediction results (53% (52%) better MAE than best neural operator (non-neural operator deep learning model)) compared to state-of-the-art forecasting models. 

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5. NON-LINEAR OPERATOR APPROXIMATIONS FOR INITIAL VALUE PROBLEMS

   Gupta, Gaurav; Xiao, Xiongye; Balan, Radu; Bogdan, Paul (April 2022, International Conference on Learning Representations (ICLR))

   Time-evolution of partial differential equations is fundamental for modeling several complex dynamical processes and events forecasting, but the operators associated with such problems are non-linear. We propose a Pad´e approximation based exponential neural operator scheme for efficiently learning the map between a given initial condition and the activities at a later time. The multiwavelets bases are used for space discretization. By explicitly embedding the exponential operators in the model, we reduce the training parameters and make it more data-efficient which is essential in dealing with scarce and noisy real-world datasets. The Pad´e exponential operator uses a recurrent structure with shared parameters to model the non-linearity compared to recent neural operators that rely on using multiple linear operator layers in succession. We show theoretically that the gradients associated with the recurrent Pad´e network are bounded across the recurrent horizon. We perform experiments on non-linear systems such as Korteweg-de Vries (KdV) and Kuramoto–Sivashinsky (KS) equations to show that the proposed approach achieves the best performance and at the same time is data-efficient. We also show that urgent real-world problems like epidemic forecasting (for example, COVID- 19) can be formulated as a 2D time-varying operator problem. The proposed Pad´e exponential operators yield better prediction results (53% (52%) better MAE than best neural operator (non-neural operator deep learning model)) compared to state-of-the-art forecasting models. 

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