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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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120240359479 · Jun 202019922001200920172026
48 results for divergence free property

New proof finds three divergence-free vector fields for any 3D manifold.

problem Proving the existence of divergence-free vector fields on 3D manifolds.
method Using geometric properties of eigenspinors in three dimensions.
result Found three divergence-free vector fields that are orthogonal and have the same length at every point.

We study in a uniform manner the properties of biconservative surfaces in arbitrary Riemannian manifolds. Biconservative surfaces being characterized by the vanishing of the divergence of a symmetric tensor field S2S_2 of type (1,1)(1,1), their properties will follow from general properties of a symmetric tensor field of …

2017-04-15abs ↗pdf ↗

Study finds rigidity of biconservative hypersurfaces in space forms without curvature assumptions.

problem Investigating biconservative hypersurfaces in space forms without scalar curvature assumptions.
method Introduced a novel divergence-free tensor to derive results without curvature assumptions.
result Rigidity results for biconservative hypersurfaces in space forms without scalar curvature assumptions.

Study characterizes non-collapsed RCD(K, N) spaces using heat kernel metrics.

problem Characterize non-collapsed RCD(K, N) spaces via heat kernel metrics.
method Investigate the second principal term in heat kernel metrics and prove divergence free property.
result Proves non-collapsed property via divergence free property of heat kernel metrics.

New Lie-group methods preserve geometric divergence-free features on manifolds.

problem Designing divergence-free Lie-group methods on manifolds.
method Introducing planar aromatic trees to span the free tracial post-Lie-Rinehart algebra.
result New Lie-group methods derived for high-order accuracy.

Researchers solve a 25-year-old conjecture about vector fields.

problem Proving a 25-year-old conjecture about divergence-free vector fields.
method Analysis of a Leibniz algebra underlying these vector fields.
result Construction of the universal central extension for divergence-free vector fields and diffeomorphisms.

We obtain explicitly all solutions of the SU(infinity) Toda field equation with the property that the associated Einstein-Weyl space admits a 2-sphere of divergence-free shear-free geodesic congruences. The solutions depend on an arbitrary holomorphic function and give rise to new hyperKahler and selfdual Einstein metr…

1999-11-16abs ↗pdf ↗

Conditions found for linearizing divergence-free fields on invariant tori.

problem Linearizing divergence-free vector fields on invariant tori.
method Assuming a solution to the cohomological equation and using Bers' results in pseudo-analytic function theory.
result The field BB on SS is either identically zero or nowhere vanishing, with a linearizable form.

We prove that a gradient shrinking Ricci soliton with fourth order divergence-free Riemannian tensor is rigid. For the 44-dimensional case, we show that any gradient shrinking Ricci soliton with fourth order divergence-free Riemannian tensor is either Einstein, or a finite quotient of the Gaussian shrinking soliton $\…

2017-05-27abs ↗pdf ↗

New model-free DR-RL algorithm with finite sample complexity.

problem Limited model-free DR-RL methods with convergence guarantees or sample complexities.
method Integrates Multi-level Monte Carlo (MLMC) technique with threshold mechanism.
result First model-free DR-RL approach with finite sample complexity for total variation and Chi-square divergence.

We study the problem of finding strain-minimising stream surfaces in a divergence-free vector field. These surfaces are generated by motions of seed curves that propagate through the field in a strain minimising manner, i.e., they move without stretching or shrinking, preserving the length of their arbitrary arc. In ge…

2014-11-05abs ↗pdf ↗

Bregman divergences play a central role in the design and analysis of a range of machine learning algorithms. This paper explores the use of Bregman divergences to establish reductions between such algorithms and their analyses. We present a new scaled isodistortion theorem involving Bregman divergences (scaled Bregman…

2016-07-01abs ↗pdf ↗

The paper explores statistical and topological properties of sliced probability divergences.

problem Understanding the topological, statistical, and computational consequences of slicing divergences.
method Deriving theoretical properties of sliced probability divergences, including metric axioms preservation and weak continuity.
result Sliced divergences share similar topological properties and have stable sample complexity.

This paper deals with the problem of describing the vector spaces of divergence-free, natural tensors on a pseudo-Riemannian manifold that are second-order; i.e., that are defined using only second derivatives of the metric. The main result establishes isomorphisms between these spaces and certain spaces of tensors (at…

2013-06-18abs ↗pdf ↗

A one-to-one correspondence is drawn between law invariant risk measures and divergences, which we define as functionals of pairs of probability measures on arbitrary standard Borel spaces satisfying a few natural properties. Divergences include many classical information divergence measures, such as relative entropy a…

2015-10-23abs ↗pdf ↗

A {\em 2-Riemannian manifold} is a differentiable manifold exhibiting a 2-inner product on each tangent space. We first study lower dimensional 2-Riemannian manifolds by giving necessary and sufficient conditions for flatness. Afterward we associate to each 2-Riemannian manifold a unique torsion free compatible pseudoc…

2012-01-10abs ↗pdf ↗

New rigidity results for quasi-Einstein metrics with non-zero divergence-free vector fields.

problem Classifying quasi-Einstein metrics with specific vector field properties.
method Analyzing quasi-Einstein metrics on closed manifolds and near-horizon geometries of extreme black holes.
result These metrics always admit a one-parameter group of isometries generated by the divergence-free vector field.

Gaussian processes improved for ocean current reconstruction and divergence identification.

problem Reconstructing ocean currents from sparse buoy data.
method Proposed a Helmholtz decomposition-based approach to Gaussian processes for better physical modeling.
result Improved inference on ocean currents and divergence identification with minimal computational cost.

Electrostatic systems with specific tensors are locally conformally flat.

problem Understanding the geometry of electrostatic systems with special tensors.
method Proving local conformal flatness for electrostatic manifolds with divergence-free Bach tensor.
result Three-dimensional electrostatic manifolds with divergence-free Bach tensor are locally conformally flat.

New theorem links symmetries to first integrals in plasma physics.

problem Understanding the relationship between symmetries and first integrals in divergence-free fields.
method Developed a Noether-type Theorem reformulation for three-dimensional divergence-free vector fields.
result Converse of the Noether-type Theorem holds on the toroidal region, proving the existence of flux coordinates.

Study compares statistical properties and power of divergence measures for credit risk monitoring.

problem Detecting distributional shifts in credit risk models.
method Derives statistical properties and chi-square benchmark values for Jensen-Shannon Divergence and Kullback-Leibler Divergence, demonstrating their applicability in credit risk monitoring.
result Jensen-Shannon Divergence and Kullback-Leibler Divergence follow chi-square distributions and reveal practical trade-offs in minimizing false positives vs. detecting changes.

Formula derived for sample complexity in binary hypothesis testing.

problem Determine the minimum number of samples to distinguish between two distributions.
method Developed a formula for sample complexity in both prior-free and Bayesian settings, using Jensen-Shannon and Hellinger divergences.
result Formula characterizes sample complexity for a wide range of error parameters, up to multiplicative constants.

Dual-ISL improves implicit generative model training with convex optimization and explicit density approximation.

problem Training implicit generative models with robust and practical likelihood-free objectives.
method Introduces dual-ISL, a novel likelihood-free objective using a convex divergence derived from the invariant statistical loss (ISL) framework.
result Dual-ISL yields a convex optimization problem in the space of model densities, providing explicit density approximation and improved training stability.

Study shows TAP free energy minimization provides better posterior inference in high-dimensional linear models.

problem Deviation from true posterior mean and underestimation of posterior uncertainty in variational inference.
method Minimization of TAP free energy in a high-dimensional asymptotic framework, showing geometric and statistical properties.
result Local minimizer of TAP free energy provides consistent estimate of posterior marginals and correctly calibrated posterior inference.

We generalise the average asymptotic linking number of a pair of divergence-free vector fields on homology three-spheres by considering the linking of a divergence-free vector field on a manifold of arbitrary dimension with a codimension two foliation endowed with an invariant transverse measure. We prove that the aver…

2001-11-19abs ↗pdf ↗

We classify complete gradient Ricci solitons satisfying a fourth-order vanishing condition on the Weyl tensor, improving previously known results. More precisely, we show that any nn-dimensional (n4n\geq 4) gradient shrinking Ricci soliton with fourth order divergence-free Weyl tensor is either Einstein, or a finite q…

2016-02-01abs ↗pdf ↗

New method learns disentangled signals without prior or model constraints.

problem Learning disentangled signals from data without prior or model constraints.
method Minimizes conditional KL divergence using a sequential algorithm to learn de-mixing flow models.
result Method learns self-sufficient signals that can reconstruct missing values.