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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.

168,657 papers · 148 categories

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0111 · Dec 201819922001200920172026
6 results for RT-equations

Extends optimal regularity and Uhlenbeck compactness to non-Riemannian manifolds.

problem Establishing optimal regularity and compactness for connections on vector bundles over non-Riemannian manifolds.
method Proofs based on RT-equations for connections with LpL^p curvature, extending to non-compact gauge groups.
result Removes singularities at GR shock waves, ensuring existence of geodesics and coordinates.

Extends optimal regularity and compactness to vector bundles over non-Riemannian manifolds.

problem Optimal regularity and compactness for connections on vector bundles.
method Derive RT-equations, establish existence theory, handle curvature up to L1L^1.
result Optimal regularity and compactness extended to vector bundles over non-Riemannian manifolds.

We present authors' new theory of the RT-equations, nonlinear elliptic partial differential equations which determine the coordinate transformations which smooth connections ΓΓ to optimal regularity, one derivative smoother than the Riemann curvature tensor Riem(Γ){\rm Riem}(Γ). As one application we extend Uhlenbeck compa…

2018-12-14abs ↗pdf ↗

The paper extends Hawking's singularity theorem to metrics with Hölder continuity and bounded curvature.

problem Proving singularity theorems for metrics with low regularity.
method Combining elliptic RT-equations for metric regularisation and manifold convolution for curvature refinement.
result Establishes globally hyperbolic and timelike incompleteness for metrics with Hölder continuity and bounded curvature.

X-TFC solves parametric DEs with neural networks and physics constraints.

problem Solving parametric differential equations with physics constraints.
method Combines Theory of Functional Connections and Physics-Informed Neural Networks with a single-layer Extreme Learning Machine.
result Achieves high accuracy with low computational time.