Oberwolfach Publications (Mathematisches Forschungsinst. Oberwolfach)
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    2063 research outputs found

    Bounded Weight Modules for Basic Classical Lie Superalgebras at Infinity

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    We classify simple bounded weight modules over the complex simple Lie superalgebras sl()\mathfrak{sl}(\infty |\infty) and osp(m2n)\mathfrak{osp} (m | 2n), when at least one of mm and nn equals \infty. For osp(m2n)\mathfrak{osp} (m | 2n) such modules are of spinor-oscillator type, i.e., they combine into one the known classes of spinor o(m)\mathfrak{o} (m)-modules and oscillator-type sp(2n)\mathfrak{sp} (2n)-modules. In addition, we characterize the category of bounded weight modules over osp(m2n)\mathfrak{osp} (m | 2n) (under the assumption dimosp(m2n)=\dim \, \mathfrak{osp} (m | 2n) = \infty) by reducing its study to already known categories of representations of sp(2n)\mathfrak{sp} (2n), where nn possibly equals \infty. When classifying simple bounded weight sl()\mathfrak{sl}(\infty |\infty)-modules, we prove that every such module is integrable over one of the two infinite-dimensional ideals of the Lie algebra sl()0ˉ\mathfrak{sl}(\infty |\infty)_{\bar{0}}. We finish the paper by establishing some first facts about the category of bounded weight sl()\mathfrak{sl} (\infty |\infty)-modules

    Discretization of Inherent ODEs and the Geometric Integration of DAEs with Symmetries

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    Discretization methods for differential-algebraic equations (DAEs) are considered that are based on the integration of an associated inherent ordinary differential equation (ODE). This allows to make use of any discretization scheme suitable for the numerical integration of ODEs. For DAEs with symmetries it is shown that the inherent ODE can be constructed in such a way that it inherits the symmetry properties of the given DAE and geometric properties of its flow. This in particular allows the use of geometric integration schemes with a numerical flow that has analogous geometric properties

    Jahresbericht | Annual Report - 2021

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    Moduli spaces and Modular forms (hybrid meeting)

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    The relation between moduli spaces and modular forms goes back to the theory of elliptic curves. On the one hand both topics experience their own growth and development, but from time to time new unexpected links show up and usually these lead to progress on both sides. One subject where there has been a lot of progress concerns the moduli of abelian varieties and K3 surfaces and especially on the Kodaira dimension of these spaces. The idea of the workshop was to bring together the experts of the two areas in the hope that discussion, interaction and lectures would spur the development of new ideas. The lectures of the workshop gave ample evidence of the interaction and provided opportunities for further interaction. Besides the lectures participants interacted via zoom in smaller groups

    On the Computational Content of the Theory of Borel Equivalence Relations

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    This preprint offers computational insights into the theory of Borel equivalence relations. Specifically, we classify equivalence relations on the Cantor space up to computable reductions, i.e., reductions induced by Turing functionals. The presented results correspond to three main research focuses: (i) the poset of degrees of equivalence relations on reals under computable reducibility; (ii) the complexity of the equivalence relations generated by computability-theoretic reducibilities (T,tt,m,1)(\leqslant_T , \leqslant_{tt} , \leqslant_m , \leqslant_1 ), (iii) the effectivization of the notion of hyperfiniteness

    Reflections on hyperbolic space

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    In school, we learn that the interior angles of any triangle sum up to pi. However, there exist spaces different from the usual Euclidean space in which this is not true. One of these spaces is the ''hyperbolic space'', which has another geometry than the classical Euclidean geometry. In this snapshot, we consider the geometry of hyperbolic polytopes, for example polygons, how they tile hyperbolic space, and how reflections along the faces of polytopes give rise to important mathematical structures. The classification of these structures is an open area of research

    Ultrafilter methods in combinatorics

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    Given a set X, ultrafilters determine which subsets of X should be considered as large. We illustrate the use of ultrafilter methods in combinatorics by discussing two cornerstone results in Ramsey theory, namely Ramsey’s theorem itself and Hindman’s theorem. We then present a recent result in combinatorial number theory that verifies a conjecture of Erdos known as the “B + C conjecture”

    Jahresbericht | Annual Report - 2020

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    Challenges in Optimization with Complex PDE-Systems (hybrid meeting)

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    The workshop concentrated on various aspects of optimization problems with systems of nonlinear partial differential equations (PDEs) or variational inequalities (VIs) as constraints. In particular, discussions around several keynote presentations in the areas of optimal control of nonlinear or non-smooth systems, optimization problems with functional and discrete or switching variables leading to mixed integer nonlinear PDE constrained optimization, shape and topology optimization, feedback control and stabilization, multi-criteria problems and multiple optimization problems with equilibrium constraints as well as versions of these problems under uncertainty or stochastic influences, and the respectively associated numerical analysis as well as design and analysis of solution algorithms were promoted. Moreover, aspects of optimal control of data-driven PDE constraints (e.g. related to machine learning) were addressed

    Deep Learning for Inverse Problems (hybrid meeting)

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    Machine learning and in particular deep learning offer several data-driven methods to amend the typical shortcomings of purely analytical approaches. The mathematical research on these combined models is presently exploding on the experimental side but still lacking on the theoretical point of view. This workshop addresses the challenge of developing a solid mathematical theory for analyzing deep neural networks for inverse problems

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    Oberwolfach Publications (Mathematisches Forschungsinst. Oberwolfach)
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