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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.

168,695 papers · 148 categories

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20 results for Michell Truss

New method calculates volume-renormalized mass from Hamiltonian perspective.

problem Calculating volume-renormalized mass for asymptotically hyperbolic manifolds.
method Using Michel's mass invariants and a reduced Hamiltonian perspective, the volume-renormalized mass is deduced.
result The reduced Hamiltonian recovers the volume-renormalized mass and its variations.

Consider a Hamiltonian action by a compact Lie group on a possibly noncompact symplectic manifold. We give a short proof of a geometric formula for decomposition into irreducible representations of the equivariant index of a Spinc^c-Dirac operator in this context. This formula was conjectured by Michèle Vergne in 2006…

2015-09-08abs ↗pdf ↗

We study Dehn surgeries on null-homotopic knots that yield fibred 33--manifolds when an additional (but natural) homological restriction is imposed. The major tool used is Gabai's theory of sutured manifold decomposition. Such surgeries are negative examples to a question of Michel Boileau. Another result we will prov…

2007-12-28abs ↗pdf ↗

The construction of characteristic classes via the curvature form of a connection is one motivation for the refinement of integral cohomology by de Rham cocycles -- known as differential cohomology. We will discuss the analog in the case of a group action on the manifold: The definition of equivariant characteristic fo…

2015-10-21abs ↗pdf ↗

Structural optimization is a popular method for designing objects such as bridge trusses, airplane wings, and optical devices. Unfortunately, the quality of solutions depends heavily on how the problem is parameterized. In this paper, we propose using the implicit bias over functions induced by neural networks to impro…

2019-09-10abs ↗pdf ↗

Study of conjugacy classes in infinite-type surfaces' mapping class groups.

problem Characterizing conjugacy classes in infinite-type surfaces' mapping class groups.
method Model-theoretic methods developed by Kechris, Rosendal, and Truss.
result Detailed classification of conjugacy classes in mapping class groups of infinite-type surfaces.

Paper proposes a closed-form formula for geometric Istanbul call options.

problem Pricing geometric Istanbul call options under the Black-Scholes model.
method Second-order Taylor expansion to derive a closed-form approximation.
result The proposed formula accurately approximates GIC values compared to Monte-Carlo simulations.

An iterated function system ΦΦ consisting of contractive similarity mappings has a unique attractor FRdF \subseteq \mathbb{R}^d which is invariant under the action of the system, as was shown by Hutchinson [Hut]. This paper shows how the action of the function system naturally produces a tiling T\mathcal{T} of the con…

2006-06-05abs ↗pdf ↗

A generalization of the Euler-Plateau problem to account for the energy contribution due to twisting of the bounding loop is proposed. Euler-Lagrange equations are derived in a parameterized setting and a bifurcation analysis is performed. A pair of dimensionless parameters govern bifurcations from a flat, circular gro…

2014-05-07abs ↗pdf ↗

SOLO uses DNN to optimize complex topology problems with reduced FEM calculations.

problem Optimizing materials distribution in complex domains with high computational cost.
method Integrates DNN with FEM calculations to learn and substitute objective functions dynamically.
result Optimum predicted by DNN converges to true global optimum through iterations.

This paper proposes a new VoI analysis framework for complex decision problems.

problem Optimizing resource allocation for information collection in decision-making under uncertainty.
method Surrogate-based framework for Value of Information analysis, integrating knowledge sharing and adaptive training.
result Accurate and robust estimates of VoI with fewer model evaluations compared to state-of-the-art methods.

This paper develops a geometric framework for SHM using feature bundles and gauge theories.

problem Defining feature spaces in population-based SHM.
method Introduces feature bundles as sections of vector bundles over structure spaces, and uses Graph Neural Networks for feature assignment.
result Develops a mathematical framework for SHM that incorporates feature spaces and gauge theories.